Alternative Data Provider Market Analysis The alternative data provider market is projected to reach a value of USD 1252 million in 2025, exhibiting a CAGR of 9% during the forecast period 2025-2033. Key drivers of this growth include increasing demand for actionable insights, the rise of artificial intelligence (AI) and machine learning (ML) technologies, and the need for real-time data analysis. The market is segmented into application areas such as BFSI, industrial, IT and telecommunications, retail and logistics, and others; and data types including credit card transactions, consultants, web data, sentiment and public data, and others. The market is highly competitive, with established players such as Preqin, Dataminr, YipitData, and S&P Global holding significant market share. However, the entry of new players and the development of innovative technologies are expected to intensify competition in the future. The geographical distribution of the market highlights the dominance of North America, followed by Europe and Asia Pacific. The adoption of alternative data is expected to be particularly strong in emerging markets, as organizations seek to gain a competitive edge by leveraging data-driven decision-making.

Alternative Data Market size was valued at USD 16.13 Billion in 2024 and is projected to reach USD 408.72 Billion by 2031, growing at a CAGR of 54.92% from 2024 to 2031.

Global Alternative Data Market Drivers

Growing Need for Alpha Generation: Investors are continuously looking for fresh sources of alpha, or excess returns over a benchmark, in the fiercely competitive financial markets. Insights from alternative data are distinct from those from traditional sources, which helps investors spot opportunities and obtain a competitive advantage. Technological Developments: The mass gathering and examination of alternative data has been made easier by technological developments, especially in fields like artificial intelligence, machine learning, and big data analytics. These technologies improve the value proposition of alternative data for investors by enabling complex data processing, pattern detection, and predictive modeling. Proliferation of Data Sources: Beyond traditional financial and economic indicators, there is a proliferation of data sources due to the internet and digital technology. Web traffic, satellite imagery, social media feeds, consumer transactions, and sensor data are just a few examples of the many sources that make up alternative data, which offers deep and varied insights into a number of fields and industries. Regulatory Environment: The gathering, storing, and use of alternative data may be affected by changes in regulations, such as the General Data Protection Regulation (GDPR) of the European Union and other comparable data protection legislation across the globe. Adherence to regulatory mandates is crucial for alternative data providers and consumers, as it molds the market environment and impacts data procurement tactics.

The Alternative Data Services market is experiencing robust growth, driven by the increasing demand for non-traditional data sources among financial institutions and investment firms. The market's expansion is fueled by several key factors. Firstly, the need for enhanced investment strategies and improved risk management is pushing firms to explore alternative data sources beyond traditional financial statements. This includes incorporating web scraping, social media sentiment analysis, satellite imagery, and transactional data to gain a competitive edge in market prediction and portfolio management. Secondly, advancements in data analytics and machine learning capabilities have made it easier to process and interpret this complex, unstructured alternative data, leading to more actionable insights. Finally, the rising availability of alternative data providers, many specializing in niche data segments, has fostered a dynamic and competitive market. While the exact market size in 2025 is unavailable, a reasonable estimation based on a plausible CAGR of 25% (a common growth rate for rapidly expanding technology sectors) from a hypothetical base year 2019 figure of $5 Billion, would place the 2025 market size at approximately $15 billion. This estimate acknowledges the market's dynamic nature and potential for faster or slower growth based on economic conditions and technological advancements. However, the upward trend remains undeniable. The market's segmentation includes various data types and service models. Companies are categorized into providers specializing in specific data sources (e.g., transactional data, satellite imagery) and those offering integrated platforms that combine multiple data types. Geopolitically, North America currently dominates the market, given the concentration of financial institutions and technology firms in the region. However, significant growth is expected from Asia-Pacific and Europe, driven by increasing adoption of alternative data in developing financial markets. Restraints include challenges related to data quality, regulation, and data privacy concerns. The increasing regulatory scrutiny around the use of alternative data necessitates robust compliance strategies for both data providers and users. Despite these challenges, the long-term outlook for the Alternative Data Services market remains extremely positive, with a projected substantial increase in market size over the next decade. This growth will be driven by continuous technological innovation, expanding data availability, and the increasing demand for data-driven investment decision-making.

The Alternative Data Provider market, currently valued at $1.252 billion (2025), is experiencing robust growth, projected to expand at a Compound Annual Growth Rate (CAGR) of 9% from 2025 to 2033. This expansion is driven by several key factors. Firstly, the increasing demand for more granular and timely insights across various sectors – BFSI (Banking, Financial Services, and Insurance), industrial, IT and telecommunications, retail and logistics – fuels the adoption of alternative data sources beyond traditional financial data. Secondly, the sophistication of analytical techniques and AI/ML-powered solutions allows for more effective processing and interpretation of diverse data types, including credit card transactions, web data, sentiment analysis, and public records. This enables businesses to make more informed, data-driven decisions. Finally, the emergence of specialized providers catering to niche needs within these sectors has created a competitive yet innovative marketplace. While regulatory hurdles and data privacy concerns pose challenges, the overall market trajectory remains positive, indicating strong potential for future growth and investment. The market segmentation reveals a diverse landscape. Application-wise, BFSI currently holds a significant share due to the sector's reliance on real-time insights for risk management and investment strategies. However, the IT and telecommunications and Retail and Logistics sectors are exhibiting strong growth potential, driving demand for alternative data solutions to improve operational efficiency and customer understanding. Regarding data types, credit card transactions and web data are currently dominant, but sentiment and public data are gaining traction due to their ability to provide nuanced understanding of market trends and consumer behavior. Leading companies such as Preqin, Dataminr, and others are constantly innovating their offerings, focusing on the development of advanced analytics and data integration capabilities to capture a larger market share in this dynamic space. Geographical expansion, particularly in the Asia-Pacific region driven by increasing digital adoption and economic growth, presents significant opportunities for future market expansion.

The Alternative Data Provider market, currently valued at $1252 million in 2025, is projected to experience robust growth, driven by increasing demand for data-driven insights across diverse sectors. A compound annual growth rate (CAGR) of 9% from 2025 to 2033 indicates a significant expansion of this market. Key drivers include the rising adoption of alternative data sources like credit card transactions, web data, and social media sentiment analysis for investment strategies, risk management, and improved business decision-making. The BFSI (Banking, Financial Services, and Insurance) sector is a major adopter, leveraging alternative data to enhance credit scoring, fraud detection, and customer segmentation. The growing sophistication of AI and machine learning algorithms further fuels market expansion by enabling more efficient processing and analysis of diverse data streams. While data privacy regulations present a potential restraint, the market's growth trajectory suggests that innovative solutions and increased regulatory clarity will mitigate these challenges. The market segmentation, encompassing various application areas and data types, indicates a diversified ecosystem with opportunities for specialized providers and integrated platforms. Companies like Preqin, Dataminr, and Bloomberg Second Measure are key players shaping this dynamic landscape through their innovative data solutions and analytics capabilities. Geographic expansion, particularly in regions with burgeoning financial technology and digital infrastructure, such as Asia-Pacific, will contribute to the market's overall growth. The rapid adoption of alternative data is fueled by its ability to provide a more comprehensive view compared to traditional data sources. This is especially crucial in rapidly evolving markets where traditional data might lag or be insufficient. The increased availability of diverse data sources, coupled with advancements in data analytics techniques, enables financial institutions and businesses to gain a competitive edge. While challenges related to data quality, integration, and regulatory compliance remain, the overall market outlook is extremely positive. This is driven by the continuous development of more sophisticated analytics tools, a growing understanding of the value of alternative data, and increasing investments in data infrastructure. The growing number of players, including both established financial data providers and emerging technology companies, indicates a vibrant and competitive market poised for sustained growth.

The Alternative Data Vendor market is experiencing robust growth, driven by the increasing demand for non-traditional data sources to enhance investment strategies and business decision-making. The market's expansion is fueled by the proliferation of digital data, advancements in data analytics, and a growing need for more comprehensive and nuanced insights across various sectors. The BFSI (Banking, Financial Services, and Insurance) sector remains a significant driver, leveraging alternative data for credit scoring, fraud detection, and risk management. However, growth is also witnessed in industrial, IT and telecommunications, and retail and logistics sectors as businesses seek competitive advantages through data-driven decision-making. The diverse types of alternative data, including credit card transactions, web data, sentiment analysis, and public data, cater to a wide range of applications. While data privacy and regulatory concerns pose challenges, the market is overcoming these hurdles through robust data anonymization and compliance strategies. The competitive landscape features both established players like S&P Global and Bloomberg, along with emerging technology-driven companies, fostering innovation and market expansion. We project a steady compound annual growth rate (CAGR) resulting in a substantial market expansion over the next decade. This growth is expected to be distributed across regions, with North America and Europe maintaining leading positions due to early adoption and developed data infrastructure. The forecast period from 2025 to 2033 anticipates continued market expansion, propelled by factors such as increasing data availability from IoT devices, refined analytical techniques, and expanding applications across new sectors. The market's segmentation by application and data type is expected to further evolve, with niche players focusing on specific data sets and industries. This specialized approach allows for deeper insights and catering to specific client needs. Geographic expansion will continue, with growth in Asia-Pacific particularly driven by the increasing adoption of digital technologies and expanding economic activity. Strategic partnerships and mergers and acquisitions will likely shape the competitive landscape, fostering consolidation and further innovation in alternative data solutions. Despite challenges related to data quality, security, and ethical considerations, the overall outlook for the Alternative Data Vendor market remains highly positive, with substantial growth opportunities over the long term.

The Alternative Data Platform market is experiencing robust growth, driven by the increasing demand for non-traditional data sources within the financial services sector. The market's expansion is fueled by several key factors: the rise of quantitative investment strategies that heavily rely on alternative data for alpha generation; the growing sophistication of data analytics techniques capable of extracting meaningful insights from complex datasets; and the increasing availability of diverse alternative data streams, including social media sentiment, satellite imagery, and transactional data. This market is segmented across various data types (e.g., web traffic, social media, satellite imagery), industry verticals (e.g., finance, retail, healthcare), and deployment models (cloud-based, on-premise). The competitive landscape is characterized by both established players and emerging fintech companies, leading to ongoing innovation and consolidation. We estimate the market size in 2025 to be $5 billion, with a compound annual growth rate (CAGR) of 25% projected through 2033. This signifies substantial future opportunities for vendors and investors alike. Significant trends shaping this market include the increasing adoption of cloud-based platforms for scalability and cost-effectiveness, the rise of AI-powered data analytics for enhanced insight extraction, and a greater focus on data security and regulatory compliance. However, challenges remain. These include the high cost of alternative data acquisition and processing, the need for specialized expertise in data science and analytics, and concerns related to data quality and bias. Despite these restraints, the overall market outlook is positive, with continued growth driven by the expanding use of alternative data across a broader range of industries and investment strategies. The competitive landscape includes companies like Accelex, Exabel, Similarweb, Preqin, and many others actively innovating and expanding their offerings to meet the evolving needs of the market. This ongoing innovation and competition ensure a dynamic and rapidly changing marketplace.

The Alternative Data Vendor market is experiencing robust growth, driven by increasing demand for non-traditional data sources across diverse sectors. The market's expansion is fueled by the need for more granular and timely insights than traditional data sources can provide. Financial institutions (BFSI), in particular, are heavily investing in alternative data to enhance risk assessment, improve fraud detection, and gain a competitive edge in investment strategies. The adoption of alternative data is also accelerating in the industrial, IT & telecommunications, and retail & logistics sectors, where it's utilized for optimizing supply chains, improving customer targeting, and enhancing operational efficiency. The various types of alternative data, including credit card transactions, web data, and sentiment analysis, cater to specific business needs, contributing to the market's segmentation. While challenges such as data privacy concerns and the need for sophisticated data processing capabilities exist, the overall market trajectory remains positive. We estimate the 2025 market size to be $8 billion, growing at a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This growth is projected to be driven by increasing data availability, advancements in data analytics, and a growing awareness among businesses of the value of alternative data insights. The North American region currently holds the largest market share due to early adoption and the presence of key players, but the Asia-Pacific region is projected to witness significant growth in the coming years. This growth is fueled by increasing digitization and the rising adoption of advanced analytics within various industry sectors. The competitive landscape is characterized by a mix of established players and emerging startups. Established firms like S&P Global and Bloomberg offer comprehensive data solutions alongside their traditional offerings, while newer companies like Dataminr and Preqin specialize in providing niche alternative data sets. This competitive environment fosters innovation and drives the development of new data sources and analytics capabilities. The ongoing consolidation and partnerships within the industry suggest a trend towards integrated platforms that combine different types of alternative data, allowing businesses to access more holistic and comprehensive insights. Continued investment in AI and machine learning technologies will further enhance the capabilities of alternative data vendors, enabling the extraction of more meaningful insights from complex datasets. The continued focus on data security and compliance will also shape the market's development, leading to the implementation of more robust data governance frameworks.

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The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public land and voluntarily provided private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastre Theme ( https://communities.geoplatform.gov/ngda-cadastre/ ). The PAD-US is an ongoing project with several published versions of a spatial database including areas dedicated to the preservation of biological diversity, and other natural (including extraction), recreational, or cultural uses, managed for these purposes through legal or other effective means. The database was originally designed to support biodiversity assessments; however, its scope expanded in recent years to include all open space public and nonprofit lands and waters. Most are public lands owned in fee (the owner of the property has full and irrevocable ownership of the land); however, permanent and long-term easements, leases, agreements, Congressional (e.g. 'Wilderness Area'), Executive (e.g. 'National Monument'), and administrative designations (e.g. 'Area of Critical Environmental Concern') documented in agency management plans are also included. The PAD-US strives to be a complete inventory of U.S. public land and other protected areas, compiling “best available” data provided by managing agencies and organizations. The PAD-US geodatabase maps and describes areas using thirty-six attributes and five separate feature classes representing the U.S. protected areas network: Fee (ownership parcels), Designation, Easement, Marine, Proclamation and Other Planning Boundaries. An additional Combined feature class includes the full PAD-US inventory to support data management, queries, web mapping services, and analyses. The Feature Class (FeatClass) field in the Combined layer allows users to extract data types as needed. A Federal Data Reference file geodatabase lookup table (PADUS3_0Combined_Federal_Data_References) facilitates the extraction of authoritative federal data provided or recommended by managing agencies from the Combined PAD-US inventory. This PAD-US Version 3.0 dataset includes a variety of updates from the previous Version 2.1 dataset (USGS, 2020, https://doi.org/10.5066/P92QM3NT ), achieving goals to: 1) Annually update and improve spatial data representing the federal estate for PAD-US applications; 2) Update state and local lands data as state data-steward and PAD-US Team resources allow; and 3) Automate data translation efforts to increase PAD-US update efficiency. The following list summarizes the integration of "best available" spatial data to ensure public lands and other protected areas from all jurisdictions are represented in the PAD-US (other data were transferred from PAD-US 2.1). Federal updates - The USGS remains committed to updating federal fee owned lands data and major designation changes in annual PAD-US updates, where authoritative data provided directly by managing agencies are available or alternative data sources are recommended. The following is a list of updates or revisions associated with the federal estate: 1) Major update of the Federal estate (fee ownership parcels, easement interest, and management designations where available), including authoritative data from 8 agencies: Bureau of Land Management (BLM), U.S. Census Bureau (Census Bureau), Department of Defense (DOD), U.S. Fish and Wildlife Service (FWS), National Park Service (NPS), Natural Resources Conservation Service (NRCS), U.S. Forest Service (USFS), and National Oceanic and Atmospheric Administration (NOAA). The federal theme in PAD-US is developed in close collaboration with the Federal Geographic Data Committee (FGDC) Federal Lands Working Group (FLWG, https://communities.geoplatform.gov/ngda-govunits/federal-lands-workgroup/ ). 2) Improved the representation (boundaries and attributes) of the National Park Service, U.S. Forest Service, Bureau of Land Management, and U.S. Fish and Wildlife Service lands, in collaboration with agency data-stewards, in response to feedback from the PAD-US Team and stakeholders. 3) Added a Federal Data Reference file geodatabase lookup table (PADUS3_0Combined_Federal_Data_References) to the PAD-US 3.0 geodatabase to facilitate the extraction (by Data Provider, Dataset Name, and/or Aggregator Source) of authoritative data provided directly (or recommended) by federal managing agencies from the full PAD-US inventory. A summary of the number of records (Frequency) and calculated GIS Acres (vs Documented Acres) associated with features provided by each Aggregator Source is included; however, the number of records may vary from source data as the "State Name" standard is applied to national files. The Feature Class (FeatClass) field in the table and geodatabase describe the data type to highlight overlapping features in the full inventory (e.g. Designation features often overlap Fee features) and to assist users in building queries for applications as needed. 4) Scripted the translation of the Department of Defense, Census Bureau, and Natural Resource Conservation Service source data into the PAD-US format to increase update efficiency. 5) Revised conservation measures (GAP Status Code, IUCN Category) to more accurately represent protected and conserved areas. For example, Fish and Wildlife Service (FWS) Waterfowl Production Area Wetland Easements changed from GAP Status Code 2 to 4 as spatial data currently represents the complete parcel (about 10.54 million acres primarily in North Dakota and South Dakota). Only aliquot parts of these parcels are documented under wetland easement (1.64 million acres). These acreages are provided by the U.S. Fish and Wildlife Service and are referenced in the PAD-US geodatabase Easement feature class 'Comments' field. State updates - The USGS is committed to building capacity in the state data-steward network and the PAD-US Team to increase the frequency of state land updates, as resources allow. The USGS supported efforts to significantly increase state inventory completeness with the integration of local parks data in the PAD-US 2.1, and developed a state-to-PAD-US data translation script during PAD-US 3.0 development to pilot in future updates. Additional efforts are in progress to support the technical and organizational strategies needed to increase the frequency of state updates. The PAD-US 3.0 included major updates to the following three states: 1) California - added or updated state, regional, local, and nonprofit lands data from the California Protected Areas Database (CPAD), managed by GreenInfo Network, and integrated conservation and recreation measure changes following review coordinated by the data-steward with state managing agencies. Developed a data translation Python script (see Process Step 2 Source Data Documentation) in collaboration with the data-steward to increase the accuracy and efficiency of future PAD-US updates from CPAD. 2) Virginia - added or updated state, local, and nonprofit protected areas data (and removed legacy data) from the Virginia Conservation Lands Database, provided by the Virginia Department of Conservation and Recreation's Natural Heritage Program, and integrated conservation and recreation measure changes following review by the data-steward. 3) West Virginia - added or updated state, local, and nonprofit protected areas data provided by the West Virginia University, GIS Technical Center. For more information regarding the PAD-US dataset please visit, https://www.usgs.gov/gapanalysis/PAD-US/. For more information about data aggregation please review the PAD-US Data Manual available at https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-manual . A version history of PAD-US updates is summarized below (See https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-history for more information): 1) First posted - April 2009 (Version 1.0 - available from the PAD-US: Team pad-us@usgs.gov). 2) Revised - May 2010 (Version 1.1 - available from the PAD-US: Team pad-us@usgs.gov). 3) Revised - April 2011 (Version 1.2 - available from the PAD-US: Team pad-us@usgs.gov). 4) Revised - November 2012 (Version 1.3) https://doi.org/10.5066/F79Z92XD 5) Revised - May 2016 (Version 1.4) https://doi.org/10.5066/F7G73BSZ 6) Revised - September 2018 (Version 2.0) https://doi.org/10.5066/P955KPLE 7) Revised - September 2020 (Version 2.1) https://doi.org/10.5066/P92QM3NT 8) Revised - January 2022 (Version 3.0) https://doi.org/10.5066/P9Q9LQ4B Comparing protected area trends between PAD-US versions is not recommended without consultation with USGS as many changes reflect improvements to agency and organization GIS systems, or conservation and recreation measure classification, rather than actual changes in protected area acquisition on the ground.

The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public land and voluntarily provided private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastre Theme ( https://communities.geoplatform.gov/ngda-cadastre/ ). The PAD-US is an ongoing project with several published versions of a spatial database including areas dedicated to the preservation of biological diversity, and other natural (including extraction), recreational, or cultural uses, managed for these purposes through legal or other effective means. The database was originally designed to support biodiversity assessments; however, its scope expanded in recent years to include all open space public and nonprofit lands and waters. Most are public lands owned in fee (the owner of the property has full and irrevocable ownership of the land); however, permanent and long-term easements, leases, agreements, Congressional (e.g. 'Wilderness Area'), Executive (e.g. 'National Monument'), and administrative designations (e.g. 'Area of Critical Environmental Concern') documented in agency management plans are also included. The PAD-US strives to be a complete inventory of U.S. public land and other protected areas, compiling “best available” data provided by managing agencies and organizations. The PAD-US geodatabase maps and describes areas using thirty-six attributes and five separate feature classes representing the U.S. protected areas network: Fee (ownership parcels), Designation, Easement, Marine, Proclamation and Other Planning Boundaries. An additional Combined feature class includes the full PAD-US inventory to support data management, queries, web mapping services, and analyses. The Feature Class (FeatClass) field in the Combined layer allows users to extract data types as needed. A Federal Data Reference file geodatabase lookup table (PADUS3_0Combined_Federal_Data_References) facilitates the extraction of authoritative federal data provided or recommended by managing agencies from the Combined PAD-US inventory. This PAD-US Version 3.0 dataset includes a variety of updates from the previous Version 2.1 dataset (USGS, 2020, https://doi.org/10.5066/P92QM3NT ), achieving goals to: 1) Annually update and improve spatial data representing the federal estate for PAD-US applications; 2) Update state and local lands data as state data-steward and PAD-US Team resources allow; and 3) Automate data translation efforts to increase PAD-US update efficiency. The following list summarizes the integration of "best available" spatial data to ensure public lands and other protected areas from all jurisdictions are represented in the PAD-US (other data were transferred from PAD-US 2.1). Federal updates - The USGS remains committed to updating federal fee owned lands data and major designation changes in annual PAD-US updates, where authoritative data provided directly by managing agencies are available or alternative data sources are recommended. The following is a list of updates or revisions associated with the federal estate: 1) Major update of the Federal estate (fee ownership parcels, easement interest, and management designations where available), including authoritative data from 8 agencies: Bureau of Land Management (BLM), U.S. Census Bureau (Census Bureau), Department of Defense (DOD), U.S. Fish and Wildlife Service (FWS), National Park Service (NPS), Natural Resources Conservation Service (NRCS), U.S. Forest Service (USFS), and National Oceanic and Atmospheric Administration (NOAA). The federal theme in PAD-US is developed in close collaboration with the Federal Geographic Data Committee (FGDC) Federal Lands Working Group (FLWG, https://communities.geoplatform.gov/ngda-govunits/federal-lands-workgroup/ ). 2) Improved the representation (boundaries and attributes) of the National Park Service, U.S. Forest Service, Bureau of Land Management, and U.S. Fish and Wildlife Service lands, in collaboration with agency data-stewards, in response to feedback from the PAD-US Team and stakeholders. 3) Added a Federal Data Reference file geodatabase lookup table (PADUS3_0Combined_Federal_Data_References) to the PAD-US 3.0 geodatabase to facilitate the extraction (by Data Provider, Dataset Name, and/or Aggregator Source) of authoritative data provided directly (or recommended) by federal managing agencies from the full PAD-US inventory. A summary of the number of records (Frequency) and calculated GIS Acres (vs Documented Acres) associated with features provided by each Aggregator Source is included; however, the number of records may vary from source data as the "State Name" standard is applied to national files. The Feature Class (FeatClass) field in the table and geodatabase describe the data type to highlight overlapping features in the full inventory (e.g. Designation features often overlap Fee features) and to assist users in building queries for applications as needed. 4) Scripted the translation of the Department of Defense, Census Bureau, and Natural Resource Conservation Service source data into the PAD-US format to increase update efficiency. 5) Revised conservation measures (GAP Status Code, IUCN Category) to more accurately represent protected and conserved areas. For example, Fish and Wildlife Service (FWS) Waterfowl Production Area Wetland Easements changed from GAP Status Code 2 to 4 as spatial data currently represents the complete parcel (about 10.54 million acres primarily in North Dakota and South Dakota). Only aliquot parts of these parcels are documented under wetland easement (1.64 million acres). These acreages are provided by the U.S. Fish and Wildlife Service and are referenced in the PAD-US geodatabase Easement feature class 'Comments' field. State updates - The USGS is committed to building capacity in the state data-steward network and the PAD-US Team to increase the frequency of state land updates, as resources allow. The USGS supported efforts to significantly increase state inventory completeness with the integration of local parks data in the PAD-US 2.1, and developed a state-to-PAD-US data translation script during PAD-US 3.0 development to pilot in future updates. Additional efforts are in progress to support the technical and organizational strategies needed to increase the frequency of state updates. The PAD-US 3.0 included major updates to the following three states: 1) California - added or updated state, regional, local, and nonprofit lands data from the California Protected Areas Database (CPAD), managed by GreenInfo Network, and integrated conservation and recreation measure changes following review coordinated by the data-steward with state managing agencies. Developed a data translation Python script (see Process Step 2 Source Data Documentation) in collaboration with the data-steward to increase the accuracy and efficiency of future PAD-US updates from CPAD. 2) Virginia - added or updated state, local, and nonprofit protected areas data (and removed legacy data) from the Virginia Conservation Lands Database, provided by the Virginia Department of Conservation and Recreation's Natural Heritage Program, and integrated conservation and recreation measure changes following review by the data-steward. 3) West Virginia - added or updated state, local, and nonprofit protected areas data provided by the West Virginia University, GIS Technical Center. For more information regarding the PAD-US dataset please visit, https://www.usgs.gov/gapanalysis/PAD-US/. For more information about data aggregation please review the PAD-US Data Manual available at https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-manual . A version history of PAD-US updates is summarized below (See https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-history for more information): 1) First posted - April 2009 (Version 1.0 - available from the PAD-US: Team pad-us@usgs.gov). 2) Revised - May 2010 (Version 1.1 - available from the PAD-US: Team pad-us@usgs.gov). 3) Revised - April 2011 (Version 1.2 - available from the PAD-US: Team pad-us@usgs.gov). 4) Revised - November 2012 (Version 1.3) https://doi.org/10.5066/F79Z92XD 5) Revised - May 2016 (Version 1.4) https://doi.org/10.5066/F7G73BSZ 6) Revised - September 2018 (Version 2.0) https://doi.org/10.5066/P955KPLE 7) Revised - September 2020 (Version 2.1) https://doi.org/10.5066/P92QM3NT 8) Revised - January 2022 (Version 3.0) https://doi.org/10.5066/P9Q9LQ4B Comparing protected area trends between PAD-US versions is not recommended without consultation with USGS as many changes reflect improvements to agency and organization GIS systems, or conservation and recreation measure classification, rather than actual changes in protected area acquisition on the ground.

The Community Credit research project explores pathways for trusted collaboration between credit unions and the communities they serve. To understand the experiences of people historically underserved by the consumer financial services industry, we focused in particular on the lived experience of low-income residents in Southern California. As part of a larger, mixed-methods study, in 2022 we mapped the landscape of financial services providers and advertisements in low-income neighborhoods in Orange County. Through documenting the presence of alternative financial services (AFS) providers and fringe financial advertisements, alongside traditional financial services providers, we investigated the spatial relationship between these businesses, as well as the factors that create consumers’ sense of (dis)trust in them. This data set contains photographs taken as part of this mapping research. All study materials and procedures were approved by the University of California, Irvine Office of..., Data was collected over the course of five trips throughout Orange County, California, between November 2021 and February 2022, yielding 420 photographs. Areas of focus were determined by utilizing the 2019 Family Financial Stability Index (FFSI; Parsons et al.), a multivariate metric developed for Orange County United Way to measure the financial stability of families with children under 18. Each trip, researchers navigated to financial services providers in neighborhoods of low family financial stability. In addition to photographing these providers, researchers drove block-by-block through the area and documented traditional and fringe financial advertisements found on telephone poles, billboards, bus shelters, and the like. Photographs were only taken in public spaces of material in plain view., Photographs are organized in folders according to trip (labeled A through E). Each photo is labeled by the trip and a number (e.g. “TripX_AdMapping_X.jpeg†). The photo directory associated with each trip contains the photo file names, descriptions and notes, and type (billboard, storefront, phone pole ad, etc.). Trip A took place in southern Santa Ana and western Orange; trip B was in northern Santa Ana and southern Anaheim; trip C was in northern Anaheim, Placentia, and Fullerton; trip D was in western Anaheim and northern Garden Grove; and trip E was in western Anaheim, northern Garden Grove, and Westminster. A map of Orange County coded according to the FFSI is included in the supplemental information (where red and dark orange indicate a neighborhood with a low score). The map also identifies local credit unions, community research partners, alternative financial services providers, and a selection of photographs from the mapping research.,

Vermont E911 Site locations (ESITEs) including buildings, facilities, and development sites; locations are represented by points. Points are attributed with addresses--composing an address points layer. Dataset is updated weekly.Field Descriptions:OBJECTID: Internal feature number. Auto-generated by Esri software.SEGMENTID: Unique segment ID.ESITEID: Unique ESITE ID.GEONAMEID: Ties ESITE to GEONAMEID (unique ID for each road name) in VT E911 Road Centerlines.PD: Prefix Direction, previously name PRE.DIR.PT: Prefix Type.SN: Street Name. Previously named STREET.ST: Street Type.SD: Suffix Direction, i.e., W for West, E for East etc.PRIMARYNAME: A concatenation of the street-name parts (PD, PT, SN, ST, SD).ALIAS1: Alternate road name.ALIAS2: Alternate road name.ALIAS3: Alternate road name.ALIAS4: Alternate road name.ALIAS5: Alternate road name.PRIMARYADDRESS: A concatenation of house number and street-name parts (PD, PT, SN, ST, SD).SITETYPE: Type of site. Uses SiteTypes domain*.TOWNNAME: Town name.MCODE: Municpal code.ESN: Emergency Service Number. Developed for each town that indicates a unique town code for each law, fire, and EMS provider. These providers are compared against the master list to determine if they are already present. If they are, the existing state code is used. If the provider is new, they are added to the state master list with the next unique provider number.ZIP: Zip code.PARCELNUM: Parcel number.GPSX: GPS X coordinate.GPSY: GPS Y coordinate.MAPYEAR: Date added to E911 data.UPDATEDATE: Update date.STATE: US State.FIPS8: Federal information processing standards codes.SPAN: Pulled from the VCGI parcel dataset via spatial join 1-3 times per year; NOT MAINTAINED DAILY.SUBTYPE: Field not in use.GlobalID_1: System-generated ID.UNITCOUNT: For commercial and residential, number of units in the site.PRIMARYADD1: Concatenation of house number, full street name, and E911 town. E911 TOWN (AKA E911 JBOUND) IS NOT ALWAYS THE SAME AS POSTAL TOWN NOR IS IT ALWAYS THE SAME AS TOWN DEFINED BY MUNICIPAL BOUNDARY. E911 TOWN (E911 JBOUND) was originally defined for the Master Street Address Guide (MSAG) Community; E911 JBOUND contains names chosen by towns for representing town names for 911 purposes.PRIMARYADD2: Concatenation of PRIMARYADD1 plus zip code.SITETYPE_MULTI1: Additional SITETYPE--if applicable. For development sites, contains the main use the site is to become. Uses SiteTypes domain*.SITETYPE_MULTI2: Additional SITETYPE--if applicable. For development sites, contains the main use the site is to become. Uses SiteTypes domain*.SITETYPE_MULTI3: Additional SITETYPE--if applicable. For development sites, contains the main use the site is to become. Uses SiteTypes domain*.SITETYPE_MULTI4: Additional SITETYPE--if applicable. For development sites, contains the main use the site is to become. Uses SiteTypes domain*.SITETYPE_MULTI5: Additional SITETYPE--if applicable. For development sites, contains the main use the site is to become. Uses SiteTypes domain*.COUNTY: County.COUNTRY: Country.SOURCEOFDATA: Source of data.DRIVEWAYID: Field not in use.ESZ: Emergency Service Zone--a defined area covered by four primary-response agencies.HOUSE_NUMBER: House number.HOUSE_NUMBERSUFFIX: For addresses not in compliance with standards (typically in urbanized areas where otherwise renumbering needs to occur). For example, a new house between 8 and 10 is built and the town calls it 8 1/2 or 8A instead of renumbering; the 1/2 or A would be in this field; there are approximately less than 300-400 of these cases.HOUSE_NUMBERPREFIX: For the three streets where alpha characters come before the house number (e.g., A20 or B12).FIPS: County FIPS number.Shape: Feature geometry.*SiteTypes Domain:ABANDONEDACCESS POINTACCESSORY BUILDINGAIR SUPPORT / MAINTENANCE FACILITYAIR TRAFFIC CONTROL CENTER / COMMAND CENTERAIRPORT TERMINALAMBULANCE SERVICEAUDITORIUM / CONCERT HALL / THEATER / OPERA HOUSEBANKBOAT RAMP / DOCKBORDER CROSSINGBORDER PATROLBUS STATION / DISPATCH FACILITYCAMPCAMPGROUNDCEMETERYCITY / TOWN HALLCOAST GUARDCOLLEGE / UNIVERSITYCOMMERCIALCOMMERCIAL CONSTRUCTION SERVICECOMMERCIAL FARMCOMMERCIAL GARAGECOMMERCIAL W/RESIDENCECOMMUNICATION BOXCOMMUNICATION TOWERCOMMUNITY / RECREATION FACILITYCOURT HOUSECULTURALCUSTOMS SERVICEDAY CARE FACILITYDEVELOPMENT SITEEBS TOWEREDUCATIONALEMERGENCY PHONE / CALLBOXFAIR / EXHIBITION/ RODEO GROUNDSFERRY TERMINAL / DISPATCH FACILITYFIRE STATIONFISH FARM / HATCHERYFITNESS FACILITYFOOD DISTRIBUTION CENTERGAS STATIONGATED W/BUILDINGGATED W/O BUILDINGGOLF COURSEGOVERNMENTGRAVEL PITGREENHOUSE / NURSERYGROCERY STOREHARBOR / MARINAHAZARDOUS MATERIALS FACILITYHAZARDOUS STORAGE FACILITYHEALTH CLINICHELIPAD / HELIPORT / HELISPOTHISTORIC SITE / POINT OF INTERESTHOSPITAL / MEDICAL CENTERHOUSE OF WORSHIPHYDROELECTRIC FACILITYICE ARENAINDUSTRIALINSTITUTIONAL RESIDENCE / DORM / BARRACKSLANDFILLLAW ENFORCEMENTLIBRARYLODGINGLOOKOUT TOWERLUMBER MILL / SAW MILLMANUFACTURING FACILITYMINEMOBILE HOMEMORGUEMULTI-FAMILY DWELLINGMUSEUMNATIONAL GUARD / ARMORYNUCLEAR FACILITYNURSING HOME / LONG TERM CAREOFFICE BUILDINGOFFICE OF EMERGENCY MANAGEMENTOIL / GAS FACILITYOTHEROTHER COMMERCIALOTHER RESIDENTIALOUTPATIENT CLINICPARK AND RIDE / COMMUTER LOTPHARMACYPICNIC AREAPOST OFFICEPRISON / CORRECTIONAL FACILITYPRIVATE AND EXPRESS SHIPPING FACILITYPSAPPUBLIC BEACHPUBLIC GATHERINGPUBLIC TELEPHONEPUBLIC WATER SUPPLY INTAKEPUBLIC WATER SUPPLY WELLPUMP STATIONRACE TRACK / DRAGSTRIPRADIO / TV BROADCAST FACILITYRAILROAD STATIONRESIDENTIAL FARMREST STOP / ROADSIDE PARKRESTAURANTRETAIL FACILITYRV HOOKUPSCHOOLSEASONAL HOMESINGLE FAMILY DWELLINGSKI AREA / ALPINE RESORTSOLAR FACILITYSPORTS ARENA / STADIUMSTATE CAPITOLSTATE GARAGESTATE GOVERNMENT FACILITYSTATE PARKSTORAGE UNITSSUBSTATIONSUGARHOUSETEMPORARY STRUCTURETOWN GARAGETOWN OFFICETRAILHEADTRANSFER STATIONUNKNOWNUS FOREST FACILITYUS GOVERNMENT FACILITYUTILITYUTILITY POLE W/PHONEVETERINARY HOSPITAL / CLINICVISITOR / INFORMATION CENTERWAREHOUSEWASTE / BIOMASS FACILITYWASTEWATER TREATMENT PLANTWATER TANKWATER TOWERWIND FACILITY / WIND TOWERYOUTH CAMP

Find alternative fueling stations near an address or ZIP code or along a route in the United States. Enter a state to see a station count. ## Data Collection Methods ## The data in the Alternative Fueling Station Locator are gathered and verified through a variety of methods. The National Renewable Energy Laboratory (NREL) obtains information about new stations from trade media, Clean Cities coordinators, an Add a Station form on the Alternative Fuels Data Center (AFDC) website, and through collaborating with infrastructure equipment and fuel providers. NREL regularly compares its station data with those of other relevant trade organizations and websites. Differences in methodologies and inclusion criteria may result in slight differences between NREL's database and those maintained by other organizations. NREL also collaborates with alternative fuel industry groups to maintain the data. NREL and its data collection subcontractor are currently collaborating with natural gas, electric drive, biodiesel, ethanol, and propane industry groups to establish best practices for identifying new stations in the most-timely manner possible and to develop a more rigorous network for the future. ## Station Update Schedule ## Existing stations in the database are contacted at least once a year on an established schedule to verify they are still operational and dispensing the fuel specified. Based on an established data collection schedule, the database is updated once a month with the exception of electric vehicle supply equipment (EVSE) data, which are updated twice a month. Stations that are no longer operational or no longer provide alternative fuel are removed from the database on a monthly basis or as they are identified. ## Mapping and Counting Methods ## Each point on the map is counted as one station in the station count. A station appears as one point on the map, regardless of the number of fuel dispensers or charging outlets at that location. Station addresses are geocoded and mapped using an automatic geocoding application. The geocoding application returns the most accurate location based on the provided address. Station locations may also be provided by external sources (e.g., station operators) and/or verified in a geographic information system (GIS) tool like Google Earth, Google Maps, or Google StreetView. This information is considered highly accurate, and these coordinates override any information generated using the geocoding application. ## Notes about Specific Station Types ## ### Private Stations ### Stations with an Access of "Private - Fleet customers only" may allow other entities to fuel through a business-to-business arrangement. For more information, fleet customers should refer to the information listed in the details section for that station to contact the station directly. ### Biodiesel Stations ### The Alternative Fueling Station Locator only includes stations offering biodiesel blends of 20% (B20) and above. ### Electric Vehicle Supply Equipment (EVSE) ### An electric charging station, or EVSE, appears as one point on the map, regardless of the number of charging outlets at that location. The number and type of charging outlets available are displayed as additional details when the station location is selected. Each point on the map is counted as one station in the station count. To see a total count of EVSE for all outlets available, go to the Alternative Fueling Station Counts by State table. Residential EVSE locations are not included in the Alternative Fueling Station Locator. ## Liquefied Petroleum Gas (Propane) Stations ### Because many propane stations serve customers other than drivers and fleets, NREL collaborated with the industry to effectively represent the differences. Each propane station is designated as a 'primary' or 'secondary' service type. Both types are able to fuel vehicles. However, locations with a 'primary' designation offer vehicle services and fuel priced specifically for use in vehicles. The details page for each station lists its service designation.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

This map shows the address, phone number information and URL for Individual Residential Alternative Provider Agencies. The main dataset is a complete listing of Provider Agencies of the following Office for People with Developmental Disabilities (OPWDD) supports and services: Intermediate Care Facilities (ICFs), Individual Residential Alternative (IRAs), Family Care, Consolidated Supports And Services, Individual Support Services (ISSs), Day Training, Day Treatment, Senior/Geriatric Services, Day Habilitation, Work Shop, Prevocational, Supported Employment Enrollments, Community Habilitation, Family Support Services, Care At Home Waiver Services, and Developmental Centers And Special Population Services. The State sector district offices (DDSOs) have remained in the Developmental Disabilities Service Provider Agencies data because they too are identified by a provider agency code that identifies the voluntary providers.

US Tariff Impact on Market

The imposition of U.S. tariffs on imported technology components, particularly software and cloud infrastructure, has created challenges for businesses in the Big Data in e-commerce market. Tariffs on components used to build cloud-based solutions and data processing software can lead to increased operational costs.

These increased costs may be passed onto e-commerce businesses, which could slow down the adoption of Big Data solutions in the short term. U.S. companies, heavily reliant on imports for these technologies, are facing disruptions in supply chains and may need to invest in domestic production or explore alternative suppliers to mitigate the impact.

Although these challenges may dampen the short-term growth, long-term demand for Big Data in e-commerce is expected to remain strong, particularly with growing reliance on data analytics for customer experience management.

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• Economic Impact: U.S. tariffs on technology components raise costs, impacting pricing for Big Data solutions and leading to increased operational expenses for e-commerce businesses.
• Geographical Impact: U.S. companies face higher prices for software and cloud solutions, resulting in a slowdown in adoption in the short term, while increasing pressure to source locally or from other regions.
• Business Impact: Tariffs on imported technology components lead to higher prices and margin compression for e-commerce companies, potentially delaying investments in Big Data solutions.

Impact Percentage on Sectors

• Software and Cloud Infrastructure: +8-10%
• E-commerce Data Solutions: +5-7%

The ODEF brings together data primarily originating from open data portals and webpages controlled by municipal and provincial governments. This database aims to enhance access to a harmonized collection of building addresses across various themes of public interest across Canada. This database is a component of the Linkable Open Data Environment (LODE). The 43 facility types used in the ODEF include:Alternative Learning CentreCampus CollégialCatholicCégepCentre Collégial De Transfert De TechnologieCentre D'EnseignementCharterCollège ConstituantCollège PrivéCollège RégionalConstituanteÉcole GouvernementaleEcs Private OperatorEntité JuridiqueÉtablissement D'EnseignementÉtablissement D'Enseignement Collège PrivéExternal Service FacilityFederal First NationsFederal JailFirst Nations SchoolFrancophoneHospitalIndependent SchoolInstallationInstallation Collège PrivéJunior CollegeMiscellaneousNursing SchoolsOrganisme Décernant Des Grades UniversitairesPrivatePrivate InstitutionPrivate SchoolProtestant SeparateProvincialPublicPublic SchoolRegroupement Administratif UniSeparateSiège SocialStrongstart BcTechnical And VocationalUniversitéUniversityFor visualization purposes, only the top 12 facility types are displayed in the map. To access the additional facilities, go to 'Symbology' and select 'Ungroup'.

Data sources and methodologyThe inputs for the ODEF are primarily datasets provided by municipal, regional or provincial sources available to the general public through open government portals under various types of open data licences, or otherwise published on their webpages and released under an open licence with their permission.

The ODEF was created by gathering the microdata on educational facilities from open data portals, provincial or territorial websites (with permission from the data owners), and one federal department.

The current version of the database (version 2.0) contains approximately 19,000 records. Collection of data from the above indicated data providers was from August 2019 to March 2021. The individual datasets were collected from their respective sources and processed and harmonized into the ODEF. Within the original datasets, each data provider attached a different set of variables. To see the full list of variables provided by a given data provider, please visit the original sources which are linked in the metadata document that accompanies the ODEF. Each facility in the ODCAF includes the following information:Institution NameInstitution TypeAuthority NameInternational Standard Classification of Education (ISCED) LevelAddressUnitStreet NumberStreet NameMunicipality NameProvincePostal CodeCensus Subdivision NameCensus Subdivision Unique IdentifierLongitudeLatitudeGeocoding SourceSource IDUnique IDFor more information on how the addresses and variables were compiled, see the metadata document that accompanies the ODEF.This is a republishing of the data available from Statistics Canada at https://www.statcan.gc.ca/en/lode/databases/odef. There were a total of 18,944 records and 3444 without cooordinates. All but 41 of the 3444 records were successfully geocoded using the Esri World Geocoder.Current Version: April 9, 2021 — Version 2.0Update Frequency: Once a year

This study had a variety of aims: (1) to assess the needs of violent crime victims, (2) to document the services that were available to violent crime victims in the San Diego region, (3) to assess the level of service utilization by different segments of the population, (4) to determine how individuals cope with victimization and how coping ability varies as a function of victim and crime characteristics, (5) to document the set of factors related to satisfaction with the criminal justice system, (6) to recommend improvements in the delivery of services to victims, and (7) to identify issues for future research. Data were collected using five different survey instruments. The first survey was sent to over 3,000 violent crime victims over the age of 16 and to approximately 60 homicide witnesses and survivors in the San Diego region (Part 1, Initial Victims' Survey Data). Of the 718 victims who returned the initial survey, 330 victims were recontacted six months later (Part 2, Follow-Up Victims' Survey Data). Respondents in Part 1 were asked what type of violent crime occurred, whether they sustained injury, whether they received medical treatment, what the nature of their relationship to the suspect was, and if the suspect had been arrested. Respondents for both Parts 1 and 2 were asked which service providers, if any, contacted them at the time of the incident or afterwards. Respondents were also asked what type of services they needed and received at the time of the incident or afterwards. Respondents in Part 2 rated the overall service and helpfulness of the information received at the time of the incident and after, and their level of satisfaction regarding contact with the police, prosecutor, and judge handling their case. Respondents in Part 2 were also asked what sort of financial loss resulted from the incident, and whether federal, state, local, or private agencies provided financial assistance to them. Finally, respondents in Part 1 and Part 2 were asked about the physical and psychological effects of their victimization. Demographic variables for Part 1 and Part 2 include the marital status, employment status, and type of job of each violent crime victim/witness/survivor. Part 1 also includes the race, sex, and highest level of education of each respondent. Police and court case files were reviewed six months after the incident occurred for each initial sample case. Data regarding victim and incident characteristics were collected from original arrest reports, jail booking screens, and court dockets (Part 3, Tracking Data). The variables for Part 3 include the total number of victims, survivors, and witnesses of violent crimes, place of attack, evidence collected, and which service providers were at the scene of the crime. Part 3 also includes a detailed list of the services provided to the victim/witness/survivor at the scene of the crime and after. These services included counseling, explanation of medical and police procedures, self-defense and crime prevention classes, food, clothing, psychological/psychiatric services, and help with court processes. Additional Part 3 variables cover circumstances of the incident, initial custody status of suspects, involvement of victims and witnesses at hearings, and case outcome, including disposition and sentencing. The race, sex, and age of each victim/witness/survivor are also recorded in Part 3 along with the same demographics for each suspect. Data for Part 4, Intervention Programs Survey Data, were gathered using a third survey, which was distributed to members of the three following intervention programs: (1) the San Diego Crisis Intervention Team, (2) the EYE Counseling and Crisis Services, Crisis and Advocacy Team, and (3) the District Attorney's Victim-Witness Assistance Program. A modified version of the survey with a subset of the original questions was administered one year later to members of the San Diego Crisis Intervention Team (Part 5, Crisis Intervention Team Survey Data) and to the EYE Counseling and Crisis Services, Crisis and Advocacy Team (Part 6, EYE Crisis and Advocacy Team Survey Data). The survey questions for Parts 4-6 asked each respondent to provide their reasons for becoming involved with the program, the goals of the program, responsibilities of the staff or volunteers, the types of referral services their agency provided, the number of hours of training required, and the topics covered in the training. Respondents for Parts 4-6 were further asked about the specific types of services they provided to victims/witnesses/survivors. Part 4 also contains a series of variables regarding coordination efforts, problems, and resolutions encountered when dealing with other intervention agencies and law enforcement agencies. Demographic variables for Parts 4-6 include the ethnicity, age, gender, and highest level of education of each respondent, and whether the respondent was a staff member of the agency or volunteer. The fourth survey was mailed to 53 referral agencies used by police and crisis interventionists (Part 7, Service Provider Survey Data). Part 7 contains the same series of variables as Part 4 on dealing with other intervention and law enforcement agencies. Respondents in Part 7 were further asked to describe the type of victims/witnesses/survivors to whom they provided service (e.g., domestic violence victims, homicide witnesses, or suicide survivors) and to rate their level of satisfaction with referral procedures provided by law enforcement officers, hospitals, paramedics, religious groups, the San Diego Crisis Intervention Team, the EYE Crisis Team, and the District Attorney's Victim/Witness Program. Part 7 also includes the hours of operation for each service provider organization, as well as which California counties they serviced. Finally, respondents in Part 7 were given a list of services and asked if they provided any of those services to victims/witnesses/survivors. Services unique to this list included job placement assistance, public awareness campaigns, accompaniment to court, support groups, and advocacy with outside agencies (e.g., employers or creditors). Demographic variables for Part 7 include the ethnicity, age, and gender of each respondent. The last survey was distributed to over 1,000 law enforcement officers from the Escondido, San Diego, and Vista sheriff's agencies (Part 8, Law Enforcement Survey Data). Respondents in Part 8 were surveyed to determine their familiarity with intervention programs, how they learned about the program, the extent to which they used or referred others to intervention services, appropriate circumstances for calling or not calling in interventionists, their opinions regarding various intervention programs, their interactions with interventionists at crime scenes, and suggestions for improving delivery of services to victims. Demographic variables for Part 8 include the rank and agency of each law enforcement respondent.

This page lists reports of individual payments to suppliers with a value over £500 made within the month. From 2014 it shows expenditure with a value over £250. Publication of these lists forms part of the Council's commitment to be open and transparent with its residents. Publication under the Transparency Code 2014. For more data and information see: http://data.surreycc.gov.uk/ where alternative data formats are available.