Thermal Infrared Multispectral Scanner (TIMS) data were collected over geologic features such as volcanic fields, playas, dunes, and alluvial fans. Each image file contains 6 bands in band-interleaved format, 8 bits per pixel. Each image is accompanied by an auxiliary file which contains the line by line temperature values of the references sources as recorded by the thermistors during each data take as well as the corresponding radiance values (in all six bands) for each thermistor as recorded by the scan head.

This feature layer spatially represents DriveNC//NCDOT’s TIMS incident data feed by Road Condition. The TIMS Incidents data contains the general location of and details about incidents that affect or will affect travel on roads maintained by the North Carolina Department of Transportation.This hosted feature layer view is intended for public viewing.NCDOT TIMS Incidents Points - Contains a point reference for all incidents, point and line.NCDOT TIMS Incidents Lines - Lines representing linear incidents. Generally, only select incidents types and road closed conditions will be represented as lines. A point feature will represent the same incident.This data includes all incidents including those recorded on concurrent routes. To remove these from the data, filter where "CreatedFromConcurrent = False".The data is provided through an automated routine that pulls data from the TIMS geojson feed every 5 minutes. Note that the data may disappear for a few moments while being refreshed.The feature layer contains all incidents; current and future. To only view current incidents in your web map, create a filter using the "StartDate" field. For example, filter by Start Date "in the last" 6 years. Note: DateTime fields are in UTC (as indicated in the field name). Text fields contain data/time values in Eastern Time.

Attributes/Fields:

Attribute/Field 


Description/Comments

Id

Unique Id of the Incident

Location

General description of the incident location.

Road

The road on which the incident occurred.

CommonName

Common name of the road on which the incident occurred or will occur

Direction

Direction of traffic impacted. Values include: North South East West All Inner Out

RouteType

Type of Route. Values include: Interstate US Route NC Route Secondary Road

RouteSuffix

Route Suffix. Values include: Alternate Business Bypass Connector Express Truck Toll

RouteId

NCDOT route id

City

Name of the nearest city to the incident.

CountyId

County number in which the incident occurred. Values from 1 to 100, where Alamance is 1 and Yancey is 100.

CountyName

Name of the county in which the incident occurred.

Division

Division number

EventId

Id of the Event designation. If the field is blank, the incident is not associated with an event.

EventName

Name of the Event designation. If the field is blank, the incident is not associated with an event.

Reason

Additional information about the incident.

IncidentType

Type of incident. Values include: Emergency Road Work Construction Night Time Construction Weekend Construction Maintenance Night Time Maintenance Road Obstruction Vehicle Crash Disabled Vehicle Congestion Signal Problem Weather Event Fog Fire Special Event Other Reported Incident

Severity

Severity of the incident. Values include: 1 = Low Impact 2 = Medium Impact 3 – High Impact

Condition

Road condition caused by the incident. Congestion Lane Closed Lane Shift Lanes Closed Lanes Narrowed Moving Closure Permanent Road Closure Ramp Closed Rest Area Closed Road Closed Road Impassable Shoulder Closed Lane Narrowed Ramp Lane Closed Ramp Lane Narrowed Road Closed with Detour

Detour

The detour or alternate route instructions

LanesClosed

The total number of lanes closed due to the incident

LanesTotal

The total number of lanes affected by the incident

DriveNCLink

Link to the DriveNC web page for the incident

StartDateUTC

Incident start date/time in UTC. AGOL automatically adjust date/time to the local time zone. Calculate the time when used outside of AGOL. To calculate EST subtract 5 hours. To calculate EDT subtract 4 hours.

EndDateUTC

Incident end date/time in UTC. AGOL automatically adjust date/time to the local time zone. Calculate the time when used outside of AGOL. To calculate subtract 5 hours. To calculate EDT subtract 4 hours.

LastUpdateDateUTC

Last update date/time in UTC. AGOL automatically adjust date/time to the local time zone. Calculate the time when used outside of AGOL. To calculate subtract 5 hours. To calculate EDT subtract 4 hours

TIMCCreationDateUTC

TIMS Creation date/time in UTC. Calculate the time when used outside of AGOL. To calculate EST subtract 5 hours.To calculate EDT subtract 4 hours

StartDateET

Incident Start Date in ET (EDT or EST).This is a text field suitable for display. Use the UTC fields for filtering on time.

StartTimeET

Incident Start Time in ET (EDT or EST). This is a text field suitable for display. Use the UTC fields for filtering on time.

EndDateET

Incident End Date in ET (EDT or EST). This is a text field suitable for display. Use the UTC fields for filtering on time.

EndTimeET

Incident End Time in ET (EDT or EST). This is a text field suitable for display. Use the UTC fields for filtering on time.

LastUpdateDateET

Last Update Date in ET (EDT or EST). This is a text field suitable for display. Use the UTC fields for filtering on time.

LastUpdateTimeET

Last Update Time in ET (EDT or EST). This is a text field suitable for display. Use the UTC fields for filtering on time.

TIMSCreationDateET

TIMS Creation Date in ET (EDT or EST). This is a text field suitable for display. Use the UTC fields for filtering on time.

TIMSCreationTimeET

TIMS Creation Time in ET (EDT or EST). This is a text field suitable for display. Use the UTC fields for filtering on time.

Latitude

Latitude

Longitude

Longitude

Note: Details about the incident are available through the NCDOT TIMS site. You can link directly to the incident details by combining https://tims.ncdot.gov/TIMS/IncidentDetail.aspx?id= and the TimsId/Incident ID. Note: The Last Modified and Created dates apply to this item entry in GO!NC/ArcGIS Online and may not reflect the actual dates of the data or map service itself.

Data independent acquisition modes isolate and concurrently fragment populations of different precursors by cycling through segments of a predefined precursor m/z range. Although these selection windows collectively cover the entire m/z range, overall only a few percent of all incoming ions in each window are sampled. Making use of the correlation of molecular weight and ion mobility in a trapped ion mobility device (timsTOF Pro), we here devise a novel scan mode that samples up to 100% of the peptide precursor ion current in m/z and mobility windows. We extend an established targeted data extraction workflow by including the ion mobility dimension for both signal extraction and scoring, thereby increasing the specificity for precursor identification. Data acquired from whole proteome digests and mixed organism samples demonstrate deep proteome coverage and a very high degree of reproducibility as well as quantitative accuracy, even from 10 ng sample amounts.

The real-time index database market is experiencing robust growth, driven by the increasing demand for immediate insights from large volumes of data across diverse sectors. The market's expansion is fueled by the proliferation of IoT devices generating massive real-time data streams, the need for faster decision-making in competitive environments, and the rise of sophisticated analytics applications requiring rapid data access. Cloud-based solutions dominate the market due to their scalability, cost-effectiveness, and ease of deployment, attracting both individual users and large enterprises. However, concerns around data security and latency in cloud-based systems present some restraints. The on-premises segment, while smaller, continues to cater to businesses with stringent data sovereignty requirements or those managing exceptionally sensitive information. Key players like Elastic, Amazon Web Services, Apache Solr, Splunk, and Microsoft are shaping the market landscape through continuous innovation and competitive offerings. Geographic distribution reflects the concentration of technological infrastructure and data generation, with North America and Europe currently leading the market, followed by the Asia-Pacific region showing significant potential for future growth. The market's Compound Annual Growth Rate (CAGR) suggests a consistent upward trajectory, indicating continued investment and market expansion throughout the forecast period. The competitive dynamics are marked by a mix of established players and emerging entrants. Established players leverage their existing infrastructure and customer bases, while new entrants focus on niche areas and innovative solutions. The market is also witnessing increased adoption of hybrid models combining cloud and on-premises solutions to balance cost-efficiency, security, and performance. Future growth will depend on technological advancements, particularly in areas like distributed ledger technology and edge computing, which will enhance the real-time capabilities and scalability of index databases. Furthermore, the increasing focus on data governance and regulatory compliance will also influence market adoption and shape the development of future solutions. The market is anticipated to witness a sustained period of growth, fueled by the ever-growing demand for real-time data analytics and insights across various sectors and regions.

Explore a full description of the map.This map layer shows the 24 time zones commonly used in the Greenwich Mean Time model. The hours added or subtracted from the time in Greenwich are marked on the map. For example, if it is 1:00 p.m. in London, England, United Kingdom, it is 6:30 pm in New Delhi, Delhi, India (+5.50), and 5:00 a.m. in Los Angeles, California, United States (-8.00). CreditsEsri, from National Geographic MapMakerTerms of Use This work is licensed under the Esri Master License Agreement.View Summary | View Terms of Use

This dataset contains a sample of the broadcast Traveler Information Messages (TIM) being generated by the Wyoming Connected Vehicle (CV) Pilot. This dataset only contains SchemaVersion 6 TIM sample data from December 18, 2018 to present. It is updated hourly and will hold up to 3 million of the most recent TIM records. The Schema Version 6 data is described further here. For sample TIM data prior to December 18, 2018, please refer to the Schema Version 5 dataset. The full set of TIMs can be found in the ITS Sandbox.

Rosalia Times Series Database

The BOKU (University of Natural Resources and Life Sciences Vienna) university demonstration forest Rosalia with an area of 950 ha has been used for research and education since 1875. In 2013 – upon an initiative of a group of researchers in various disciplines – it was decided to extend the so far mainly forestry oriented activities by implementing a hydrological experimental research watershed. The overall objective is to collect data that support the study of transport processes in the system of soil, water, plants and atmosphere. More specifically, emphasis is on bridging the gap between point related measurements and effective values and parameters required for modelling flow and transport processes in watersheds.

2 Objectives

The main objectives for the research watershed are

to collect data that support the study of transport processes in the system of soil, water, plants and atmosphere

emphasis is on bridging the gap between point related measurements and effective values and parameters for modelling watersheds of various sizes

to generate comprehensive reference information for research projects on future management and climate change impacts

Operation is planned for a period of at least 10 years using only internal resources of the university, to avoid potential interruptions due to project-based short-term availability of personal and financial resources.

The objective of this article is to present the research watershed, the data collected and to make these data accessible to the research community.

A. SUMMARY This table contains all victims (parties who are injured) involved in a traffic crash resulting in an injury in the City of San Francisco. Fatality year-to-date crash data is obtained from the Office of the Chief Medical Examiner (OME) death records, and only includes those cases that meet the San Francisco Vision Zero Fatality Protocol maintained by the San Francisco Department of Public Health (SFDPH), San Francisco Police Department (SFPD), and San Francisco Municipal Transportation Agency (SFMTA). Injury crash data is obtained from SFPD’s Interim Collision System for 2018 to YTD, Crossroads Software Traffic Collision Database (CR) for years 2013-2017 and the Statewide Integrated Transportation Record System (SWITRS) maintained by the California Highway Patrol for all years prior to 2013. Only crashes with valid geographic information are mapped. All geocodable crash data is represented on the simplified San Francisco street centerline model maintained by the Department of Public Works (SFDPW). Collision injury data is queried and aggregated on a quarterly basis. Crashes occurring at complex intersections with multiple roadways are mapped onto a single point and injury and fatality crashes occurring on highways are excluded.

The crash, party, and victim tables have a relational structure. The traffic crashes table contains information on each crash, one record per crash. The party table contains information from all parties involved in the crashes, one record per party. Parties are individuals involved in a traffic crash including drivers, pedestrians, bicyclists, and parked vehicles. The victim table contains information about each party injured in the collision, including any passengers. Injury severity is included in the victim table.

For example, a crash occurs (1 record in the crash table) that involves a driver party and a pedestrian party (2 records in the party table). Only the pedestrian is injured and thus is the only victim (1 record in the victim table).

B. HOW THE DATASET IS CREATED Traffic crash injury data is collected from the California Highway Patrol 555 Crash Report as submitted by the police officer within 30 days after the crash occurred. All fields that match the SWITRS data schema are programmatically extracted, de-identified, geocoded, and loaded into TransBASE. See Section D below for details regarding TransBASE.

C. UPDATE PROCESS After review by SFPD and SFDPH staff, the data is made publicly available approximately a month after the end of the previous quarter (May for Q1, August for Q2, November for Q3, and February for Q4).

D. HOW TO USE THIS DATASET This data is being provided as public information as defined under San Francisco and California public records laws. SFDPH, SFMTA, and SFPD cannot limit or restrict the use of this data or its interpretation by other parties in any way. Where the data is communicated, distributed, reproduced, mapped, or used in any other way, the user should acknowledge TransBASE.sfgov.org as the source of the data, provide a reference to the original data source where also applicable, include the date the data was pulled, and note any caveats specified in the associated metadata documentation provided. However, users should not attribute their analysis or interpretation of this data to the City of San Francisco. While the data has been collected and/or produced for the use of the City of San Francisco, it cannot guarantee its accuracy or completeness. Accordingly, the City of San Francisco, including SFDPH, SFMTA, and SFPD make no representation as to the accuracy of the information or its suitability for any purpose and disclaim any liability for omissions or errors that may be contained therein. As all data is associated with methodological assumptions and limitations, the City recommends that users review methodological documentation associated with the data prior to its analysis, interpretation, or communication.

This dataset can also be queried on the TransBASE Dashboard. TransBASE is a geospatially enabled database maintained by SFDPH that currently includes over 200 spatially referenced variables from multiple agencies and across a range of geographic scales, including infrastructure, transportation, zoning, sociodemographic, and collision data, all linked to an intersection or street segment. TransBASE facilitates a data-driven approach to understanding and addressing transportation-related health issues, informed by a large and growing evidence base regarding the importance of transportation system design and land use decisions for health. TransBASE’s purpose is to inform public and private efforts to improve transportation system safety, sustainability, community health and equity in San Francisco.

E. RELATED DATASETS Traffic Crashes Resulting in Injury Traffic Crashes Resulting in Injury: Parties Involved TransBASE Dashboard iSWITRS TIMS

Global On premises Real time Database market size 2025 was XX Million. On premises Real time Database Industry compound annual growth rate (CAGR) will be XX% from 2025 till 2033.

Report of Real-time Database is covering the summarized study of several factors encouraging the growth of the market such as market size, market type, major regions and end user applications. By using the report customer can recognize the several drivers that impact and govern the market. The report is describing the several types of Real-time Database Industry. Factors that are playing the major role for growth of specific type of product category and factors that are motivating the status of the market.

The real-time traffic data market is experiencing robust growth, driven by the increasing need for efficient transportation management and urban planning. This market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This significant expansion is fueled by several key factors. The proliferation of connected vehicles and the rise of smart cities are generating massive volumes of traffic data, creating a high demand for real-time insights. Government agencies are increasingly leveraging this data for optimizing traffic flow, improving infrastructure, and enhancing public safety. Furthermore, the logistics and automotive sectors are benefiting from improved route planning, fleet management, and predictive maintenance capabilities enabled by real-time traffic data. The market's segmentation, encompassing various data types (traffic data, mobility data, car traffic data) and applications (government, logistics, infrastructure construction, automobile), reflects its diverse utility across multiple industries. The continued expansion of this market is expected to be driven by advancements in data analytics, the adoption of 5G technology enabling faster data transmission, and the growing integration of IoT devices in vehicles and infrastructure. However, challenges remain, including data privacy concerns, the high cost of data acquisition and processing, and the need for robust data security measures to maintain the integrity and reliability of the information. Competition among established players like TomTom, HERE, and INRIX, and the emergence of innovative startups, is likely to further shape market dynamics and accelerate innovation in data processing and analytical tools within the foreseeable future. Specific regional growth will vary, with North America and Europe currently dominating the market share, while Asia-Pacific is anticipated to experience the fastest growth due to rapid urbanization and technological advancements in the region.

Real-time database to accompany revision triangles, by quarter, chained volume measures, seasonally adjusted, UK.

Impervious surfaces are surfaces that do not allow water to pass through. Examples of these surfaces include highways, parking lots, rooftops, and airport runways. Instead of allowing rain to pass into the soil, impervious surfaces cause water to collect at the surface, then run off. An increase in impervious surface area causes an increase of water volume which needs to be managed by stormwater systems. With the flow come pollutants, which collect on impervious surfaces then discharge with the runoff into streams and the ocean. Runoff water does not enter the water table, and that can cause other management issues, such as interruptions in baseline stream flow.The NLCD imperviousness layer represents urban impervious surfaces as a percentage of developed surface over every 30-meter pixel in the United States. Phenomenon Mapped: The proportion of the landscape that is impervious to water.Time Extent: 2001, 2004, 2006, 2008, 2011, 2013, 2016, 2019, and 2021 for the lower 48 conterminous US states. A small portion of Alaska around Anchorage displays a time series of 2001, 2011, and 2016. Hawaii, Puerto Rico, and the US Virgin Islands unfortunately only have data for 2001 so there is only one image in the series. This information may be used in conjunction with the USA NLCD Land Cover layer.Units: PercentCell Size: 30 metersSource Type: DiscretePixel Type: Unsigned integerData Coordinate System: North America Albers Equal Area Conic (102008)Mosaic Projection: North America Albers Equal Area Conic (102008)Extent: CONUS, Hawaii, A portion of Alaska around Anchorage, District of Columbia, Puerto RicoNoData Value: 127Source: Multi-Resolution Land Characteristics ConsortiumPublication Date: June 30, 2023ArcGIS Server URL: https://landscape10.arcgis.com/arcgis/Time SeriesBy default, this layer will appear in your client with a time slider which allows you to play the series as an animation. The animation will advance year by year, but the layer only changes appearance every few years in the lower 48 states, in 2001, 2004, 2006, 2008, 2011, 2013, 2016, 2019, and 2021. To select just one year in the series, first turn the time series off on the time slider, then create a definition query on the layer which selects only the desired year.Time Series DescriptorMRLC issued a set of companion rasters with this impervious surface layer showing the reason why each pixel is impervious. This companion layer, called the Developed Imperviousness Descriptor, is not currently available in this map service. The descriptor layer identifies types of roads, core urban areas, and energy production sites for each impervious pixel to allow deeper analysis of developed features. The descriptor layer may be downloaded directly from MRLC and added to ArcGIS Pro.Alaska, Hawaii, and Puerto RicoAt this time Alaska, Hawaii, and Puerto Rico are produced with a different methodology, and are not set up to be directly compared the way the CONUS time series is. To analyze change between the latest two data years for this portion of Alaska, be sure to use the NLCD 2011 to 2016 Developed Impervious Change raster. For Hawaii and Puerto Rico, only the year 2001 is available for download at the MRLC.North America Albers ProjectionAll NLCD layers in the Living Atlas are projected into the North America Albers Projection before serving in the Living Atlas. This allows the coterminous USA, Puerto Rico, Hawaii, and Alaska to be served from a common projection and analyzed together. In tests performed by esri, the NLCD land cover classes after projection to North America Albers had the exact same number of pixels in input as output, but pixels had been slightly rearranged after projection. Processing TemplatesThis layer comes with two color schemes, cool and warm. The default is a cool gray color scheme, designed to look good on light and dark gray web maps. To choose a warm color scheme which was the default until 2021, change the processing template to the Impervious Surface Warm Renderer in your map client.Dataset SummaryThe National Land Cover Database products are created through a cooperative project conducted by the Multi-Resolution Land Characteristics Consortium (MRLC). The MRLC Consortium is a partnership of federal agencies, consisting of the U.S. Geological Survey, the National Oceanic and Atmospheric Administration, the U.S. Environmental Protection Agency, the U.S. Department of Agriculture, the U.S. Forest Service, the National Park Service, the U.S. Fish and Wildlife Service, the Bureau of Land Management and the USDA Natural Resources Conservation Service.What can you do with this layer?This layer can be used to create maps and to visualize the underlying data. This layer can be used as an analytic input in ArcGIS Desktop.This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.

Travel time data is collected in real-time from commercial vehicles and has been provided in this dataset for four separate weeks in 2016 and for two months in 2017.

2016 TTDS Data:

\* Monday 25 July 2016 - Sunday 31 July 2016

\* Monday 8 August to Sunday 14th August 2016

\* Monday 21 November 2016 – Sunday 27 November 2016

\* Monday 26 December 2016 – Sunday 1 January 2017 (school holidays and New Year’s Eve)

2017 TTDS Data:

\* September 2017

\* October 2017

Please refer to the Roads Realtime data which provides the same underlying data as the Road Travel Time data presented here.

The fields for this data set include the Position Time, the GPS location, the Bearing in degrees, the speed in KPH and the Speed Limit for that section.

This dataset contains raw Signal Phasing and Timing (SPaT), MAP, and Basic Safety Messages (BSM) data from the "Feasibility Study and Assessment of Communications Approaches for Real-Time Traffic Signal Applications" project in the hexadecimal string and pcap formats. The project characterizes and assesses the feasibility of SPaT messages for infrastructure-based safety applications by comparing messages received through cellular networks with those received through Dedicated Short Range Communication (DSRC). This dataset contains the raw research data collected for the project. The project's final report and supporting dataset can be found at the National Transportation Library and is linked in the references section of this dataset.

VITAL SIGNS INDICATOR Commute Time (T3)

FULL MEASURE NAME Commute time by residential location

LAST UPDATED April 2020

DESCRIPTION Commute time refers to the average number of minutes a commuter spends traveling to work on a typical day. The dataset includes metropolitan area, county, city, and census tract tables by place of residence.

DATA SOURCE U.S. Census Bureau: Decennial Census (1980-2000) - via MTC/ABAG Bay Area Census http://www.bayareacensus.ca.gov/transportation.htm

U.S. Census Bureau: American Community Survey Form B08013 (2006-2018; place of residence; overall time) Form C08136 (2006-2018; place of residence; time by mode) Form B08301 (2006-2018; place of residence) www.api.census.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) For the decennial Census datasets, breakdown of commute times was unavailable by mode; only overall data could be provided on a historical basis.

For the American Community Survey datasets, 1-year rolling average data was used for all metros, region, and county geographic levels, while 5-year rolling average data was used for cities and tracts. This is due to the fact that more localized data is not included in the 1-year dataset across all Bay Area cities. Similarly, modal data is not available for every Bay Area city or census tract, even when the 5-year data is used for those localized geographies.

Regional commute times were calculated by summing aggregate county travel times and dividing by the relevant population; similarly, modal commute time were calculated using aggregate times and dividing by the number of communities choosing that mode for the given geography. Census tract data is not available for tracts with insufficient numbers of residents.

The metropolitan area comparison was performed for the nine-county San Francisco Bay Area in addition to the primary MSAs for the nine other major metropolitan areas.

This dataset provides information about the number of properties, residents, and average property values for Tim Drive cross streets in Jonesville, SC.

NCEI Accession 0171315 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). California Polytechnic State Univeristy, San Luis Obispo, collected the data from their in-situ moored station named Cal Poly Pier San Luis Obispo in the North Pacific Ocean and Northeast Pacific Ocean. Central and Northern California Coastal Ocean Observing System (CeNCOOS), which assembles data from California Polytechnic State Univeristy, San Luis Obispo, and other sub-regional coastal and ocean observing systems of the Central and Northern California United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available.

China Railway: Turnaround Time: Truck data was reported at 4.680 Day in 2014. This records a decrease from the previous number of 4.700 Day for 2013. China Railway: Turnaround Time: Truck data is updated yearly, averaging 4.730 Day from Dec 1994 (Median) to 2014, with 21 observations. The data reached an all-time high of 5.500 Day in 1999 and a record low of 4.450 Day in 2011. China Railway: Turnaround Time: Truck data remains active status in CEIC and is reported by China Railway Corporation, National Railway Administration. The data is categorized under China Premium Database’s Transportation and Storage Sector – Table CN.TC: Railway Industry Overview.