Emissions Data Aggregation for Financial Services Market Outlook

According to our latest research, the global emissions data aggregation for financial services market size reached USD 1.85 billion in 2024, with a robust CAGR of 17.2% projected through the forecast period. By 2033, the market is anticipated to achieve a value of USD 7.15 billion, reflecting the sector's rapid expansion. Growth in this market is primarily driven by tightening regulatory frameworks, rising investor scrutiny on ESG (Environmental, Social, and Governance) factors, and the increasing adoption of digital tools for sustainability management within financial institutions.

The growth of the emissions data aggregation market in financial services is strongly influenced by the evolving regulatory landscape. Governments and regulatory bodies worldwide are implementing stricter disclosure requirements around carbon emissions and climate-related financial risks. The introduction of frameworks such as the Task Force on Climate-related Financial Disclosures (TCFD) and the European UnionÂ’s Sustainable Finance Disclosure Regulation (SFDR) has mandated banks, asset managers, and insurers to report not only their direct and indirect emissions but also those embedded across their value chains. As a result, financial institutions are seeking sophisticated data aggregation solutions to ensure compliance, minimize reputational risk, and enhance transparency for stakeholders. This regulatory momentum is expected to persist, further fueling the demand for emissions data aggregation platforms and services.

Another significant growth factor is the increasing integration of ESG criteria into investment and lending decisions. Institutional investors, asset managers, and private equity firms are under mounting pressure from clients, shareholders, and advocacy groups to align portfolios with sustainability goals and net-zero commitments. Accurate, timely, and granular emissions data has become a critical input for risk assessment, portfolio analysis, and sustainability reporting. This trend is prompting financial institutions to invest in advanced software and services capable of aggregating emissions data from diverse sources, including direct operations, energy procurement, and value chain activities. The adoption of artificial intelligence and machine learning within these solutions is further enhancing data accuracy, predictive analytics, and automated reporting capabilities, thereby driving market expansion.

Technological innovation is also playing a pivotal role in the growth of the emissions data aggregation market for financial services. Cloud-based platforms, API integrations, and blockchain technology are being leveraged to streamline data collection, validation, and reporting processes. These advancements enable financial institutions to efficiently aggregate emissions data from multiple internal and external sources, ensuring scalability and interoperability with existing IT infrastructure. Furthermore, partnerships between financial institutions and technology vendors are accelerating the development of customized solutions tailored to sector-specific needs. As digital transformation continues to reshape the financial services industry, the adoption of emissions data aggregation solutions is expected to accelerate, supporting the transition to a more sustainable and transparent financial ecosystem.

From a regional perspective, Europe currently leads the global market, driven by progressive regulatory policies and a mature ESG investment landscape. North America follows closely, with significant adoption among large banks and asset managers. The Asia Pacific region is rapidly emerging as a high-growth market, propelled by increasing regulatory alignment, investor demand for green finance, and expanding digital infrastructure. Latin America and the Middle East & Africa, while smaller in market share, are witnessing growing interest as local regulators and financial institutions begin to prioritize climate risk management and sustainability reporting. This regional diversification underscores the global relevance and growth potential of emissions data aggregation solutions in financial services.

The integration of ESG Data Feeds for Capitals is becoming increasingly vital for financial institutions aiming to enhance th

Hospital Financial Quarterly Aggregate Report

F00 - Information about the telecommunications entrepreneur F01 - Telephone services provided on the fixed public telecommunications network F02 - Interoperator cooperation in the fixed public telecommunications network F03 - Interoperator cooperation in the mobile public telecommunications network F04 - Retail services provided to end users on the mobile public telecommunications network F05 - Internet access services provided to end users F06 - Bundled services F07 - VoIP telephony services provided on the public telecommunications network F08 - Television services provided to end users

View the column descriptions here.The Office of Emergency and Resilience (OER) of the Food and Agriculture Organization (FAO) is piloting a monitoring system to better understand the impacts of COVID-19 and other shocks on food supply, agricultural livelihoods and food security in a number of food crisis countries. This project is supported by the United States Agency for International Development (USAID).The monitoring system consists of primary data collected from households and key informants (including agricultural inputs vendors, food traders and agriculture extension officers) on a periodic basis (more or less every 3 months). Data are mainly collected through Computer-Assisted Telephone Interviews (CATI). In-person surveys are conducted where the circumstances allow for field access. During each round of the system, more than 40,000 interviews have been completed in more than 20 countries. In order to associate each round of data collection with the dates it was performed, refer to the calendar available here.Data are used to guide strategic decisions, to design programmes and to inform analytical processes such as the IPC.The present layer contains data aggregated on Admin1 level, from Afghanistan, Colombia, DRC, Liberia, Mali, Niger, Sierra Leone, Somalia, Yemen and Zimbabwe. Indicator: Percentage of households reporting shocks directly or indirectly related to COVID-19

Dataset provided by = Björn Holzhauer

Dataset Description==Meta-analyses of clinical trials often treat the number of patients experiencing a medical event as binomially distributed when individual patient data for fitting standard time-to-event models are unavailable. Assuming identical drop-out time distributions across arms, random censorship and low proportions of patients with an event, a binomial approach results in a valid test of the null hypothesis of no treatment effect with minimal loss in efficiency compared to time-to-event methods. To deal with differences in follow-up - at the cost of assuming specific distributions for event and drop-out times - we propose a hierarchical multivariate meta-analysis model using the aggregate data likelihood based on the number of cases, fatal cases and discontinuations in each group, as well as the planned trial duration and groups sizes. Such a model also enables exchangeability assumptions about parameters of survival distributions, for which they are more appropriate than for the expected proportion of patients with an event across trials of substantially different length. Borrowing information from other trials within a meta-analysis or from historical data is particularly useful for rare events data. Prior information or exchangeability assumptions also avoid the parameter identifiability problems that arise when using more flexible event and drop-out time distributions than the exponential one. We discuss the derivation of robust historical priors and illustrate the discussed methods using an example. We also compare the proposed approach against other aggregate data meta-analysis methods in a simulation study.

Regulatory Reporting Data Hub for Banks Market Outlook

According to our latest research, the global Regulatory Reporting Data Hub for Banks market size reached USD 4.2 billion in 2024 and is expected to grow at a robust CAGR of 12.1% from 2025 to 2033, reaching a projected value of USD 11.7 billion by 2033. This impressive growth is primarily driven by the increasing regulatory complexity, the need for real-time data management, and the adoption of advanced digital solutions by banks worldwide. The market is witnessing a transformation as financial institutions strive to enhance transparency, streamline compliance processes, and mitigate risks associated with regulatory reporting.

One of the primary growth factors for the Regulatory Reporting Data Hub for Banks market is the rapidly evolving regulatory landscape across global banking sectors. Governments and regulatory bodies are continuously introducing new compliance standards and reporting requirements, compelling banks to upgrade their data infrastructure. The introduction of stringent regulations such as Basel III, Dodd-Frank, MiFID II, and GDPR has necessitated the deployment of robust data hubs capable of aggregating, validating, and reporting vast volumes of financial data in real time. As a result, banks are increasingly investing in advanced regulatory reporting solutions to ensure timely and accurate submission of regulatory reports, avoid penalties, and maintain their reputational integrity.

Another significant driver is the growing adoption of digital transformation strategies within the banking industry. As banks digitize their operations, there is a heightened need for centralized data management platforms that can seamlessly integrate with multiple banking systems and deliver actionable insights for regulatory compliance. Regulatory reporting data hubs offer automated data aggregation, validation, and analytics functionalities, enabling banks to reduce manual intervention and minimize errors. The integration of artificial intelligence, machine learning, and big data analytics further enhances the capabilities of these platforms, allowing banks to proactively identify compliance gaps and streamline reporting workflows. This digital shift not only improves operational efficiency but also supports banks in adapting to rapidly changing regulatory demands.

Furthermore, the increasing focus on risk management and data governance is fueling the demand for regulatory reporting data hubs. Banks are under immense pressure to maintain data accuracy, consistency, and security, especially in the face of cross-border operations and complex financial products. Regulatory reporting data hubs facilitate comprehensive data lineage, audit trails, and secure data storage, ensuring that banks can demonstrate compliance during regulatory audits. The ability to aggregate data from disparate sources and generate unified reports is becoming a strategic advantage for banks seeking to enhance their risk management frameworks and achieve regulatory alignment across multiple jurisdictions.

Regionally, North America dominates the Regulatory Reporting Data Hub for Banks market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The region's leadership is attributed to the presence of major global banks, advanced IT infrastructure, and proactive regulatory frameworks. Europe is also witnessing significant growth due to the implementation of new regulatory directives and the increasing adoption of cloud-based reporting solutions. Meanwhile, Asia Pacific is emerging as a lucrative market, driven by rapid digitalization in banking and the expansion of cross-border financial activities. Latin America and the Middle East & Africa are gradually catching up, as local banks modernize their compliance processes and embrace technology-driven reporting solutions.

Component Analysis

The Regulatory Reporting Data Hub for Banks market by component is segmented into Software and Services. The software segment holds the dominant share, as banks prioritize the deployment of comprehensive platforms capable of automating the entire regulatory reporting lifecycle. These software solutions encompass data aggregation, validation, analytics, and report generation functionalities, offering end-to-end compliance management. The evolution of cloud-native and AI-powered platforms is further enhancing the software segmen

Note: After May 3, 2024, this dataset will no longer be updated because hospitals are no longer required to report data on COVID-19 hospital admissions, hospital capacity, or occupancy data to HHS through CDC’s National Healthcare Safety Network (NHSN). The related CDC COVID Data Tracker site was revised or retired on May 10, 2023.

Note: May 3,2024: Due to incomplete or missing hospital data received for the April 21,2024 through April 27, 2024 reporting period, the COVID-19 Hospital Admissions Level could not be calculated for CNMI and will be reported as “NA” or “Not Available” in the COVID-19 Hospital Admissions Level data released on May 3, 2024.

This dataset represents COVID-19 hospitalization data and metrics aggregated to county or county-equivalent, for all counties or county-equivalents (including territories) in the United States. COVID-19 hospitalization data are reported to CDC’s National Healthcare Safety Network, which monitors national and local trends in healthcare system stress, capacity, and community disease levels for approximately 6,000 hospitals in the United States. Data reported by hospitals to NHSN and included in this dataset represent aggregated counts and include metrics capturing information specific to COVID-19 hospital admissions, and inpatient and ICU bed capacity occupancy.

Reporting information:

• As of December 15, 2022, COVID-19 hospital data are required to be reported to NHSN, which monitors national and local trends in healthcare system stress, capacity, and community disease levels for approximately 6,000 hospitals in the United States. Data reported by hospitals to NHSN represent aggregated counts and include metrics capturing information specific to hospital capacity, occupancy, hospitalizations, and admissions. Prior to December 15, 2022, hospitals reported data directly to the U.S. Department of Health and Human Services (HHS) or via a state submission for collection in the HHS Unified Hospital Data Surveillance System (UHDSS).
• While CDC reviews these data for errors and corrects those found, some reporting errors might still exist within the data. To minimize errors and inconsistencies in data reported, CDC removes outliers before calculating the metrics. CDC and partners work with reporters to correct these errors and update the data in subsequent weeks.
• Many hospital subtypes, including acute care and critical access hospitals, as well as Veterans Administration, Defense Health Agency, and Indian Health Service hospitals, are included in the metric calculations provided in this report. Psychiatric, rehabilitation, and religious non-medical hospital types are excluded from calculations.
• Data are aggregated and displayed for hospitals with the same Centers for Medicare and Medicaid Services (CMS) Certification Number (CCN), which are assigned by CMS to counties based on the CMS Provider of Services files.
• Full details on COVID-19 hospital data reporting guidance can be found here: https://www.hhs.gov/sites/default/files/covid-19-faqs-hospitals-hospital-laboratory-acute-care-facility-data-reporting.pdf

• County-level hospital data are derived using calculations performed at the Health Service Area (HSA) level. An HSA is defined by CDC’s National Center for Health Statistics as a geographic area containing at least one county which is self-contained with respect to the population’s provision of routine hospital care. Every county in the United States is assigned to an HSA, and each HSA must contain at least one hospital. Therefore, use of HSAs in the calculation of local hospital metrics allows for more accurate characterization of the relationship between health care utilization and health status at the local level.
• Data presented at the county-level represent admissions, hospital inpatient and ICU bed capacity and occupancy among hosp

The Monthly Child Care Services Data Report - Families Served by County data set includes demographic data of parents and families of children receiving Child Care and Development Fund (CCDF) assistance. The Administration for Children and Families (ACF) Office of Child Care (OCC) collects data regarding the children and families served through the Child Care and Development Fund (CCDF) as well as the types of child care settings and facilities providing services. Each quarterly data set contains data aggregated by county for each month of the quarter. Counts less than 5 are masked with an asterisk (*) to protect the confidentiality of individuals in this report.

Reporting of new Aggregate Case and Death Count data was discontinued May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. This dataset will receive a final update on June 1, 2023, to reconcile historical data through May 10, 2023, and will remain publicly available.

Aggregate Data Collection Process Since the start of the COVID-19 pandemic, data have been gathered through a robust process with the following steps:

• A CDC data team reviews and validates the information obtained from jurisdictions’ state and local websites via an overnight data review process.
• If more than one official county data source exists, CDC uses a comprehensive data selection process comparing each official county data source, and takes the highest case and death counts respectively, unless otherwise specified by the state.
• CDC compiles these data and posts the finalized information on COVID Data Tracker.
• County level data is aggregated to obtain state and territory specific totals.

Methodology Changes Several differences exist between the current, weekly-updated dataset and the archived version:

• Source: The current Weekly-Updated Version is based on county-level aggregate count data, while the Archived Version is based on State-level aggregate count data.
• Confirmed/Probable Cases/Death breakdown:  While the probable cases and deaths are included in the total case and total death counts in both versions (if applicable), they were reported separately from the confirmed cases and deaths by jurisdiction in the Archived Version.  In the current Weekly-Updated Version, the counts by jurisdiction are not reported by confirmed or probable status (See Confirmed and Probable Counts section for more detail).
• Time Series Frequency: The current Weekly-Updated Version contains weekly time series data (i.e., one record per week per jurisdiction), while the Archived Version contains daily time series data (i.e., one record per day per jurisdiction).
• Update Frequency: The current Weekly-Updated Version is updated weekly, while the Archived Version was updated twice daily up to October 20, 2022.

Confirmed and Probable Counts In this dataset, counts by jurisdiction are not displayed by confirmed or probable status. Instead, confirmed and probable cases and deaths are included in the Total Cases and Total Deaths columns, when available. Not all jurisdictions report probable cases and deaths to CDC.* Confirmed and probable case definition criteria are described here:

Council of State and Territorial Epidemiologists (ymaws.com).

Deaths CDC reports death data on other sections of the website: CDC COVID Data Tracker: Home, CDC COVID Data Tracker: Cases, Deaths, and Testing, and NCHS Provisional Death Counts. Information presented on the COVID Data Tracker pages is based on the same source (total case counts) as the present dataset; however, NCHS Death Counts are based on death certificates that use information reported by physicians, medical examiners, or coroners in the cause-of-death section of each certificate. Data from each of these pages are considered provisional (not complete and pending verification) and are therefore subject to change. Counts from previous weeks are continually revised as more records are received and processed.

Number of Jurisdictions Reporting There are currently 60 public health jurisdictions reporting cases of COVID-19. This includes the 50 states, the District of Columbia, New York City, the U.S. territories of American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, Puerto Rico, and the U.S Virgin Islands as well as three independent countries in compacts of free association with the United States, Federated States of Micronesia, Republic of the Marshall Islands, and Republic of Palau. New York State’s reported case and death counts do not include New York City’s counts as they separately report nationally notifiable conditions to CDC.

CDC COVID-19 data are available to the public as summary or aggregate count files, including total counts of cases and deaths, available by state and by county. These and other data on COVID-19 are available from multiple public locations, such as:

https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/cases-in-us.html

https://www.cdc.gov/covid-data-tracker/index.html

https://www.cdc.gov/coronavirus/2019-ncov/covid-data/covidview/index.html

https://www.cdc.gov/coronavirus/2019-ncov/php/open-america/surveillance-data-analytics.html

Additional COVID-19 public use datasets, include line-level (patient-level) data, are available at: https://data.cdc.gov/browse?tags=covid-19.

Archived Data Notes:

November 3, 2022: Due to a reporting cadence issue, case rates for Missouri counties are calculated based on 11 days’ worth of case count data in the Weekly United States COVID-19 Cases and Deaths by State data released on November 3, 2022, instead of the customary 7 days’ worth of data.

November 10, 2022: Due to a reporting cadence change, case rates for Alabama counties are calculated based on 13 days’ worth of case count data in the Weekly United States COVID-19 Cases and Deaths by State data released on November 10, 2022, instead of the customary 7 days’ worth of data.

November 10, 2022: Per the request of the jurisdiction, cases and deaths among non-residents have been removed from all Hawaii county totals throughout the entire time series. Cumulative case and death counts reported by CDC will no longer match Hawaii’s COVID-19 Dashboard, which still includes non-resident cases and deaths. 

November 17, 2022: Two new columns, weekly historic cases and weekly historic deaths, were added to this dataset on November 17, 2022. These columns reflect case and death counts that were reported that week but were historical in nature and not reflective of the current burden within the jurisdiction. These historical cases and deaths are not included in the new weekly case and new weekly death columns; however, they are reflected in the cumulative totals provided for each jurisdiction. These data are used to account for artificial increases in case and death totals due to batched reporting of historical data.

December 1, 2022: Due to cadence changes over the Thanksgiving holiday, case rates for all Ohio counties are reported as 0 in the data released on December 1, 2022.

January 5, 2023: Due to North Carolina’s holiday reporting cadence, aggregate case and death data will contain 14 days’ worth of data instead of the customary 7 days. As a result, case and death metrics will appear higher than expected in the January 5, 2023, weekly release.

January 12, 2023: Due to data processing delays, Mississippi’s aggregate case and death data will be reported as 0. As a result, case and death metrics will appear lower than expected in the January 12, 2023, weekly release.

January 19, 2023: Due to a reporting cadence issue, Mississippi’s aggregate case and death data will be calculated based on 14 days’ worth of data instead of the customary 7 days in the January 19, 2023, weekly release.

January 26, 2023: Due to a reporting backlog of historic COVID-19 cases, case rates for two Michigan counties (Livingston and Washtenaw) were higher than expected in the January 19, 2023 weekly release.

January 26, 2023: Due to a backlog of historic COVID-19 cases being reported this week, aggregate case and death counts in Charlotte County and Sarasota County, Florida, will appear higher than expected in the January 26, 2023 weekly release.

January 26, 2023: Due to data processing delays, Mississippi’s aggregate case and death data will be reported as 0 in the weekly release posted on January 26, 2023.

February 2, 2023: As of the data collection deadline, CDC observed an abnormally large increase in aggregate COVID-19 cases and deaths reported for Washington State. In response, totals for new cases and new deaths released on February 2, 2023, have been displayed as zero at the state level until the issue is addressed with state officials. CDC is working with state officials to address the issue.

February 2, 2023: Due to a decrease reported in cumulative case counts by Wyoming, case rates will be reported as 0 in the February 2, 2023, weekly release. CDC is working with state officials to verify the data submitted.

February 16, 2023: Due to data processing delays, Utah’s aggregate case and death data will be reported as 0 in the weekly release posted on February 16, 2023. As a result, case and death metrics will appear lower than expected and should be interpreted with caution.

February 16, 2023: Due to a reporting cadence change, Maine’s

Incident Feed Aggregation for Navigation Market Outlook

As per our latest research, the global Incident Feed Aggregation for Navigation market size stood at USD 2.43 billion in 2024, demonstrating a robust upward trajectory. The market is projected to reach USD 6.81 billion by 2033, growing at a remarkable CAGR of 12.1% during the forecast period from 2025 to 2033. This impressive growth is primarily driven by the increasing demand for real-time, accurate incident information to optimize navigation and ensure safety across various transportation sectors.

One of the primary growth factors for the Incident Feed Aggregation for Navigation market is the escalating need for real-time data integration in navigation systems. As transportation networks become more complex and urbanization accelerates, the ability to aggregate and analyze incident feeds—such as accidents, road closures, weather disruptions, and other hazards—has become essential. Navigation systems that leverage aggregated incident feeds can offer dynamic rerouting, minimize delays, and enhance user safety. The proliferation of connected vehicles, smart transportation infrastructure, and the adoption of IoT sensors have further amplified the volume and variety of data sources available for aggregation, making advanced incident feed solutions indispensable for both public and private sector stakeholders.

Another significant driver is the integration of advanced analytics and artificial intelligence (AI) in incident feed aggregation platforms. Modern navigation solutions are increasingly leveraging AI-powered algorithms to filter, validate, and prioritize incident reports from multiple sources, including social media, government databases, and crowdsourced inputs. This not only improves the accuracy and timeliness of incident information but also enables predictive analytics to anticipate potential disruptions before they occur. The emergence of 5G networks and edge computing has further facilitated the seamless transmission and processing of incident data in real time, empowering navigation providers to deliver highly responsive and context-aware guidance to users across road, maritime, aviation, and rail domains.

The market is also benefitting from regulatory support and increased collaboration between public agencies and private technology providers. Governments worldwide are investing in smart city initiatives and intelligent transportation systems (ITS), which prioritize the integration of incident feed aggregation into national and regional navigation platforms. Public-private partnerships are fostering innovation in data sharing and interoperability, ensuring that incident feeds are standardized and accessible to a broad ecosystem of navigation stakeholders. This collaborative approach is not only enhancing situational awareness for emergency services and transportation operators but is also driving market adoption across diverse end-user segments.

From a regional perspective, North America currently leads the Incident Feed Aggregation for Navigation market, followed closely by Europe and Asia Pacific. The high penetration of connected vehicles, advanced transportation infrastructure, and strong regulatory frameworks in North America have fostered early adoption of incident feed aggregation technologies. Europe, with its focus on cross-border transportation and data harmonization, is also witnessing significant growth. Meanwhile, Asia Pacific is emerging as a high-growth region due to rapid urbanization, increasing investments in smart mobility, and expanding logistics networks. Latin America and the Middle East & Africa are gradually catching up, driven by infrastructure modernization and rising demand for efficient navigation solutions.

Component Analysis

The Incident Feed Aggregation for Navigation market is segmented by component into software, hardware, and services, each playing a crucial role in the overall ecosystem. Software solutions form the backbone of incident feed aggregation, enabling the collection, integration, and analysis of disparate data streams. These platforms are designed to handle high volumes of real-time data, incorporating advanced analytics, machine learning, and data visualization tools to present actionable insights to navigation systems. The demand for customizable, scalable, and interoperable software is on the rise, as end-users seek solutions that can seamlessly integrate with existing navigation and

The set shall contain reports on the activities of the Register of Private Interests of the Chief Official Ethics Commission: 1. Statistics of decisions taken by the Chief Official Ethics Commission (COEC). When investigating reports, complaints and requests concerning compliance of the activities of declarants with the Law on the Adjustment of Public and Private Interests, the COEC shall decide to admit the violation, to admit the absence of an infringement or to terminate the investigation. The report shall contain data on these decisions as from 2006. 2. Statistics of declarations of private interests according to the type of position. The report shall include only the type of posts of a standard type or the type of posts indicated in the expert reports where a person lodges a declaration of his current position and of his participation in public procurement and is required to declare his post. Furthermore, if a person occupies positions of the same nature in several workplaces, only unique values of nature shall be included in the statistics. The report shall include data collected from 2020 onwards. 3. Statistics of declarations of private interest on the basis of declarations submitted and persons declaring. The report shall include data on the declarations submitted during the reporting period and the persons who submitted them. The report shall include data collected from 2012 onwards. Aggregated data shall be reported on the last day of each month. Data provider shall mean the Chief Official Ethics Commission. Contact the atverimas@stat.gov.lt for technical questions or possible errors.

After May 3, 2024, this dataset and webpage will no longer be updated because hospitals are no longer required to report data on COVID-19 hospital admissions, and hospital capacity and occupancy data, to HHS through CDC’s National Healthcare Safety Network. Data voluntarily reported to NHSN after May 1, 2024, will be available starting May 10, 2024, at COVID Data Tracker Hospitalizations. The following dataset provides state-aggregated data for hospital utilization in a timeseries format dating back to January 1, 2020. These are derived from reports with facility-level granularity across three main sources: (1) HHS TeleTracking, (2) reporting provided directly to HHS Protect by state/territorial health departments on behalf of their healthcare facilities and (3) National Healthcare Safety Network (before July 15). The file will be updated regularly and provides the latest values reported by each facility within the last four days for all time. This allows for a more comprehensive picture of the hospital utilization within a state by ensuring a hospital is represented, even if they miss a single day of reporting. No statistical analysis is applied to account for non-response and/or to account for missing data. The below table displays one value for each field (i.e., column). Sometimes, reports for a given facility will be provided to more than one reporting source: HHS TeleTracking, NHSN, and HHS Protect. When this occurs, to ensure that there are not duplicate reports, prioritization is applied to the numbers for each facility. On April 27, 2022 the following pediatric fields were added: all_pediatric_inpatient_bed_occupied all_pediatric_inpatient_bed_occupied_coverage all_pediatric_inpatient_beds all_pediatric_inpatient_beds_coverage previous_day_admission_pediatric_covid_confirmed_0_4 previous_day_admission_pediatric_covid_confirmed_0_4_coverage previous_day_admission_pediatric_covid_confirmed_12_17 previous_day_admission_pediatric_covid_confirmed_12_17_coverage previous_day_admission_pediatric_covid_confirmed_5_11 previous_day_admission_pediatric_covid_confirmed_5_11_coverage previous_day_admission_pediatric_covid_confirmed_unknown previous_day_admission_pediatric_covid_confirmed_unknown_coverage staffed_icu_pediatric_patients_confirmed_covid staffed_icu_pediatric_patients_confirmed_covid_coverage staffed_pediatric_icu_bed_occupancy staffed_pediatric_icu_bed_occupancy_coverage total_staffed_pediatric_icu_beds total_staffed_pediatric_icu_beds_coverage On January 19, 2022, the following fields have been added to this dataset: inpatient_beds_used_covid inpatient_beds_used_covid_coverage On September 17, 2021, this data set has had the following fields added: icu_patients_confirmed_influenza, icu_patients_confirmed_influenza_coverage, previous_day_admission_influenza_confirmed, previous_day_admission_influenza_confirmed_coverage, previous_day_deaths_covid_and_influenza, previous_day_deaths_covid_and_influenza_coverage, previous_day_deaths_influenza, previous_day_deaths_influenza_coverage, total_patients_hospitalized_confirmed_influenza, total_patients_hospitalized_confirmed_influenza_and_covid, total_patients_hospitalized_confirmed_influenza_and_covid_coverage, total_patients_hospitalized_confirmed_influenza_coverage On September 13, 2021, this data set has had the following fields added: on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses, on_hand_supply_therapeutic_b_bamlanivimab_courses, on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses, previous_week_therapeutic_a_casirivimab_imdevimab_courses_used, previous_week_therapeutic_b_bamlanivimab_courses_used, previous_week_therapeutic_c_bamlanivima

Unlock insights with Echo's Activity data, offering views of locations based on visitor behavior. Enhance site selection, urban planning, and real estate with metrics like unique visitors and visits. Our high-quality, global data reveals movement patterns, updated daily and normalized monthly.

This dataset includes an aggregated and event-correlated analysis of power outages in the United States, synthesized by integrating three data sources: the Environment for the Analysis of Geo-Located Energy Information (EAGLE-I), the Electric Emergency Incident Disturbance Report (DOE-417), and Annual Estimates of the Resident Population for Counties 2024 (CO-EST2024-POP). The EAGLE-I dataset, spanning from 2014 to 2023, encompasses over 146 million customers and offers county-level outage information at 15-minute intervals. The data has been processed, filtered, and aggregated to deliver an enhanced perspective on power outages, which are then correlated with DOE-417 data based on geographic location as well as the start and end times of events. For each major disturbance documented in DOE-417, essential metrics are defined to quantify the outages associated with the event. This dataset supports researchers in examining outages triggered by major disturbances like extreme weather and physical disruptions, thereby aiding studies on power system resilience.

Links to the raw data for generating the correlated dataset are included below as "DOE-417", "EAGLE-I", and "CO-EST2024-POP" resources.

Acknowledgement: This work is funded by the Laboratory Directed Research and Development (LDRD) at the Pacific Northwest National Laboratory (PNNL) as part of the Resilience Through Data-Driven, Intelligently Designed Control (RD2C) Initiative.

Ancillary Services Aggregation Market Outlook

According to our latest research, the global ancillary services aggregation market size reached USD 7.6 billion in 2024. The market is anticipated to expand at a robust CAGR of 11.2% from 2025 to 2033, propelling the industry to a forecasted value of USD 21.3 billion by 2033. This strong growth trajectory is primarily driven by the rising integration of renewable energy sources, increasing grid complexity, and the growing need for flexible, scalable solutions to maintain grid stability worldwide.

The ancillary services aggregation market is experiencing significant momentum due to the rapid adoption of renewable energy across both developed and emerging economies. As solar, wind, and other variable renewable energy sources are integrated into power grids, there is an escalating need for sophisticated ancillary services to manage frequency, voltage, and reserve requirements. Aggregators play a crucial role in pooling distributed energy resources (DERs) and enabling them to participate in ancillary service markets, thereby optimizing grid reliability and efficiency. The expansion of distributed generation and the proliferation of smart grid technologies further amplify the demand for aggregation services, as utilities and grid operators seek innovative solutions to address volatility and intermittency challenges posed by renewables.

Another major growth factor is the evolution of regulatory frameworks and market mechanisms that support the participation of aggregated resources in ancillary service markets. Many countries are updating their grid codes and market rules to allow virtual power plants (VPPs), demand response programs, and microgrids to provide essential grid services. This regulatory shift is fostering a more competitive and dynamic ancillary services landscape, enabling new entrants and business models to emerge. Additionally, advancements in digital platforms, real-time data analytics, and automated control systems are empowering aggregators to offer more responsive and reliable services, further fueling market expansion.

The increasing electrification of industrial and commercial sectors, coupled with the growing adoption of energy storage systems, is also propelling the ancillary services aggregation market forward. Industrial and commercial end-users are leveraging aggregation models to monetize their flexible loads and distributed assets, participating in frequency regulation, spinning reserve, and other ancillary service markets. This trend not only enhances revenue streams for end-users but also supports grid operators in balancing supply and demand more effectively. As energy trading platforms become more sophisticated and accessible, the role of aggregators in facilitating market participation is set to expand, unlocking new value streams across the energy ecosystem.

Regionally, North America and Europe are leading the ancillary services aggregation market, driven by mature energy markets, supportive regulatory environments, and high penetration of distributed energy resources. The Asia Pacific region, however, is emerging as a high-growth market, supported by rapid urbanization, significant investments in renewable energy, and government initiatives aimed at modernizing grid infrastructure. Latin America and the Middle East & Africa are also witnessing increased interest in ancillary services aggregation, particularly as these regions seek to enhance grid reliability and accommodate rising electricity demand. Overall, the global ancillary services aggregation market is poised for sustained growth, underpinned by technological innovation, regulatory advancements, and the ongoing transition towards cleaner, more resilient energy systems.

Service Type Analysis

The service type segment of the ancillary services aggregation market encompasses a diverse range of offerings, including frequency regulation, voltage support, spinning reserve, non-spinning reserve, black start, and other specialized services. Frequency regulation remains the largest and most critical segment, as it ensures the continuous balance between electricity supply and demand, maintaining grid stability. Aggregators leverage advanced control algorithms and real-time communication technologies to coordinate distributed resources, enabling them to respond swiftly to frequency deviations. This capability is particularly valuable in grids with high renewable penetration, where fluctuations are more frequent and pronounced. A

Incident Feed Aggregation for Navigation Market Outlook

As per our latest research, the global Incident Feed Aggregation for Navigation market size in 2024 stands at USD 2.3 billion, reflecting robust industry momentum. The market is experiencing a healthy Compound Annual Growth Rate (CAGR) of 12.1% and is projected to reach USD 6.4 billion by 2033. This growth is propelled by the increasing integration of real-time incident data into navigation systems, which is critical for ensuring safety, efficiency, and optimized route planning across multiple transportation sectors.

One of the primary growth factors for the Incident Feed Aggregation for Navigation market is the escalating demand for real-time data integration in navigation systems. With the proliferation of smart transportation solutions and the rise of connected vehicles, stakeholders across road, maritime, aviation, and rail sectors are prioritizing the need for timely and accurate incident feeds. These feeds aggregate information from diverse sources such as traffic cameras, emergency services, IoT sensors, and user-generated reports, enabling navigation platforms to provide dynamic rerouting and hazard alerts. The increasing adoption of artificial intelligence and machine learning algorithms further enhances the capability of incident feed aggregation systems, allowing for predictive analytics and proactive response, which is particularly valuable in congested urban environments and critical infrastructure corridors.

Another significant driver is the growing emphasis on public safety and regulatory compliance. Governments and transportation agencies worldwide are mandating the integration of incident feed aggregation into navigation platforms to enhance situational awareness and emergency response. For instance, the European Union’s eCall initiative and similar mandates in North America require vehicles to transmit crash data automatically to emergency services. Such regulations are pushing navigation solution providers and hardware manufacturers to invest in advanced aggregation platforms that can seamlessly collect, process, and disseminate incident information. Furthermore, the increasing frequency of natural disasters, traffic congestion, and security threats has highlighted the importance of robust incident feed systems, not only for routine navigation but also for disaster management and evacuation planning.

The market’s expansion is also facilitated by rapid technological advancements and the rising penetration of mobile devices. The proliferation of smartphones equipped with GPS and connectivity features has democratized access to real-time navigation and incident updates for individual users. Logistics companies and emergency services are leveraging cloud-based aggregation platforms to coordinate operations and optimize fleet movements. Additionally, advancements in 5G and edge computing are enabling faster data transmission and reduced latency, making incident feed aggregation more effective and reliable. The convergence of these technologies is fostering new business models and partnerships among software vendors, hardware providers, and service companies, further accelerating market growth.

From a regional perspective, North America currently dominates the Incident Feed Aggregation for Navigation market, driven by advanced infrastructure, high adoption of intelligent transportation systems, and proactive regulatory frameworks. Europe follows closely, benefiting from stringent safety regulations and significant investments in smart mobility. The Asia Pacific region is witnessing the fastest growth, fueled by rapid urbanization, expanding transportation networks, and increasing government focus on traffic management and safety. Emerging economies in Latin America and the Middle East & Africa are also investing in modernizing their navigation infrastructure, although market penetration remains relatively lower compared to developed regions. Overall, the global landscape is characterized by strong demand across all regions, with varying degrees of technological maturity and regulatory support shaping local market dynamics.

Carrier Aggregation Testing Market Outlook

According to our latest research, the global carrier aggregation testing market size reached USD 1.48 billion in 2024, reflecting robust demand for advanced mobile communication technologies. The market is projected to expand at a CAGR of 8.2% from 2025 to 2033, reaching an estimated USD 2.88 billion by 2033. This growth is primarily driven by the rapid proliferation of 5G networks, increasing requirements for higher data throughput, and the escalating complexity of wireless devices and network infrastructure. As telecom operators and device manufacturers strive to deliver seamless connectivity and superior user experiences, carrier aggregation testing has become a critical component in ensuring network reliability and optimal performance.

The growth trajectory of the carrier aggregation testing market is underpinned by several influential factors. First and foremost, the global rollout of 5G networks is accelerating the need for sophisticated testing solutions. Carrier aggregation, which enables the combination of multiple frequency bands to boost network capacity and data rates, is a cornerstone of 5G and LTE-Advanced technologies. This technical complexity necessitates advanced testing tools to validate both device and network performance under aggregated carrier scenarios. The increasing adoption of smartphones and IoT devices, all demanding higher bandwidth and lower latency, further amplifies the need for comprehensive carrier aggregation testing across diverse applications and environments.

Another significant growth driver is the relentless innovation in wireless device manufacturing. Device manufacturers are integrating multi-band capabilities and supporting a broader spectrum of frequencies to cater to global markets. This evolution requires rigorous carrier aggregation testing to ensure devices meet international standards and function seamlessly across regions. Additionally, the expansion of IoT and the emergence of connected devices in sectors such as automotive, healthcare, and smart cities are creating new testing challenges. The need to validate carrier aggregation performance in these heterogeneous environments is pushing both hardware and software testing solutions to evolve rapidly, spurring market growth.

Furthermore, network infrastructure modernization by telecom operators and network equipment manufacturers is playing a pivotal role in market expansion. As operators upgrade their networks to support advanced LTE and 5G features, the complexity of carrier aggregation configurations increases exponentially. This necessitates comprehensive in-lab and field testing to ensure network interoperability, performance, and resilience. The growing emphasis on quality of service (QoS) and user experience is compelling stakeholders to invest in state-of-the-art carrier aggregation testing solutions. These investments are not limited to developed markets; emerging economies are also ramping up their network capabilities, further fueling global demand for carrier aggregation testing.

Regionally, Asia Pacific is emerging as the dominant force in the carrier aggregation testing market, accounting for the largest share in 2024. This leadership is attributed to massive investments in 5G infrastructure by countries such as China, South Korea, and Japan, coupled with a thriving consumer electronics industry. North America and Europe are also significant contributors, driven by early adoption of advanced wireless technologies and a strong ecosystem of telecom operators and device manufacturers. Meanwhile, Latin America and the Middle East & Africa are witnessing steady growth, propelled by increasing mobile penetration and gradual network upgrades. This regional diversity ensures a broad and sustained demand for carrier aggregation testing solutions worldwide.

Offering Analysis

The offering segment of the carrier aggregation testing market encompasses hardware, software, and services, each playing a vital role in the ecosystem. Hardware solutions, including spectrum analyzers, network testers, and signal generators, form the backbone of carrier aggregation testing. These devices enable precise measurement and validation of aggregated carrier signals across multiple frequency bands. As network configurations become more complex with the advent of 5G, the demand for advanced hardware capable of supporting higher frequencies and wider bandwidths is surging. Hard

The Monthly Child Care Services Data Report - Families Served by County data set includes demographic data of parents and families of children receiving Child Care and Development Fund (CCDF) assistance. The Administration for Children and Families (ACF) Office of Child Care (OCC) collects data regarding the children and families served through the Child Care and Development Fund (CCDF) as well as the types of child care settings and facilities providing services. Each quarterly data set contains data aggregated by county for each month of the quarter. Counts less than 5 are masked with an asterisk (*) to protect the confidentiality of individuals in this report.

For a comprehensive guide to this data and other UCR data, please see my book at ucrbook.comVersion 14 release notes:Adds 2020 data. Please note that the FBI has retired UCR data ending in 2020 data so this will be the last Arrests by Age, Sex, and Race data they release. Version 13 release notes:Changes R files from .rda to .rds.Fixes bug where the number_of_months_reported variable incorrectly was the largest of the number of months reported for a specific crime variable. For example, if theft was reported Jan-June and robbery was reported July-December in an agency, in total there were 12 months reported. But since each crime (and let's assume no other crime was reported more than 6 months of the year) only was reported 6 months, the number_of_months_reported variable was incorrectly set at 6 months. Now it is the total number of months reported of any crime. So it would be set to 12 months in this example. Thank you to Nick Eubank for alerting me to this issue.Adds rows even when a agency reported zero arrests that month; all arrest values are set to zero for these rows.Version 12 release notes:Adds 2019 data.Version 11 release notes:Changes release notes description, does not change data.Version 10 release notes:The data now has the following age categories (which were previously aggregated into larger groups to reduce file size): under 10, 10-12, 13-14, 40-44, 45-49, 50-54, 55-59, 60-64, over 64. These categories are available for female, male, and total (female+male) arrests. The previous aggregated categories (under 15, 40-49, and over 49 have been removed from the data). Version 9 release notes:For each offense, adds a variable indicating the number of months that offense was reported - these variables are labeled as "num_months_[crime]" where [crime] is the offense name. These variables are generated by the number of times one or more arrests were reported per month for that crime. For example, if there was at least one arrest for assault in January, February, March, and August (and no other months), there would be four months reported for assault. Please note that this does not differentiate between an agency not reporting that month and actually having zero arrests. The variable "number_of_months_reported" is still in the data and is the number of months that any offense was reported. So if any agency reports murder arrests every month but no other crimes, the murder number of months variable and the "number_of_months_reported" variable will both be 12 while every other offense number of month variable will be 0. Adds data for 2017 and 2018.Version 8 release notes:Adds annual data in R format.Changes project name to avoid confusing this data for the ones done by NACJD.Fixes bug where bookmaking was excluded as an arrest category. Changed the number of categories to include more offenses per category to have fewer total files. Added a "total_race" file for each category - this file has total arrests by race for each crime and a breakdown of juvenile/adult by race. Version 7 release notes: Adds 1974-1979 dataAdds monthly data (only totals by sex and race, not by age-categories). All data now from FBI, not NACJD. Changes some column names so all columns are <=32 characters to be usable in Stata.Changes how number of months reported is calculated. Now it is the number of unique months with arrest data reported - months of data from the monthly header file (i.e. juvenile disposition data) are not considered in this calculation. Version 6 release notes: Fix bug where juvenile female columns had the same value as juvenile male columns.Version 5 release notes: Removes support for SPSS and Excel data.Changes the crimes that are stored in each file. There are more files now with fewer crimes per file. The files and their included crimes have been updated below.Adds in agencies that report 0 months of the year.Adds a column that indicates the number of months reported. This is generated summing up the number of unique months an agency reports data for. Note that this indicates the number of months an agency reported arrests for ANY crime. They may not necessarily report every crime every month. Agencies that did not report a crime with have a value of NA for every arrest column for that crime.Removes data on runaways.Version 4 release notes: Changes column names from "poss_coke" and "sale_coke" to "poss_heroi

The size of the Cement and Aggregate market was valued at USD 204170 million in 2023 and is projected to reach USD 244356.25 million by 2032, with an expected CAGR of 2.6% during the forecast period.