Track the FAST SIM in real-time with AIS data. TRADLINX provides live vessel position, speed, and course updates. Search by MMSI: 205011000, IMO: 9356517

You can get all global flight information in 1 API call or track flights based on flight number, airline, departure/arrival airport, and more. The data updates frequently, around every 5 minutes. The details of the data include:

Geography: Location information such as latitude, longitude, altitude, and direction. Speed: Vertical and horizontal speed of aircraft. Departure and arrival: IATA codes and ICAO codes of the departure and arrival airport. Aircraft and flight: IATA and ICAO number of flight and registration number, ICAO code, and ICAO24 code of aircraft. Airline: IATA code, and ICAO code of airline. System information: Squawk, status, and last updated in Epoch.

Here's an example response from the API: [ { "geography": { "latitude": 43.5033, "longitude": -79.1297, "altitude": 7833.36, "direction": 70 }, "speed": { "horizontal": 833.4, "isGround": 0, "vertical": 0 }, "departure": { "iataCode": "YHM", "icaoCode": "CYHM" }, "arrival": { "iataCode": "YQM", "icaoCode": "CYQM" }, "aircraft": { "icaoCode": "B763", "regNumber": "CGYAJ", "icao24": "C08412" }, "airline": { "iataCode": "W8", "icaoCode": "CJT" }, "flight": { "iataNumber": "W8620", "icaoNumber": "CJT620", "number": "620" }, "system": { "updated": 1513148168, "squawk": "0000" }, "status": "en-route" } ]

Developer Information:

1) Available Endpoints &depIata= &depIcao= &arrIata= &arrIcao= &aircraftIcao= &regNum= &aircraftIcao24= &airlineIata= &airlineIcao= &flightIata= &flightIcao= &flightNum= &status= &limit= &lat=&lng=&distance=

2) Flights Tracker API Output

Specific flight based on: Flight IATA Number: GET http://aviation-edge.com/v2/public/flights?key=[API_KEY]&flightIata=W8519

All flights of a specific Airlines: GET http://aviation-edge.com/v2/public/flights?key=[API_KEY]&airlineIata=W8

Flights from departure location: GET http://aviation-edge.com/v2/public/flights?key=[API_KEY]&depIata=MAD

Flights from arrival location: GET http://aviation-edge.com/v2/public/flights?key=[API_KEY]&arrIata=GIG

Flights within a circle area based on lat and lng values and radius as the distance: GET https://aviation-edge.com/v2/public/flights?key=[API_KEY]&lat=51.5074&lng=0.1278&distance=100&arrIata=LHR

Combinations: two airports and a specific airline flying between them: GET http://aviation-edge.com/v2/public/flights?key=[API_KEY]&depIata=ATL&arrIata=ORD&airlineIata=UA

Track the SIMAYA in real-time with AIS data. TRADLINX provides live vessel position, speed, and course updates. Search by MMSI: 525001000, IMO: 2040200

The COVID Tracking Project collects information from 50 US states, the District of Columbia, and 5 other US territories to provide the most comprehensive testing data we can collect for the novel coronavirus, SARS-CoV-2. We attempt to include positive and negative results, pending tests, and total people tested for each state or district currently reporting that data.

Testing is a crucial part of any public health response, and sharing test data is essential to understanding this outbreak. The CDC is currently not publishing complete testing data, so we’re doing our best to collect it from each state and provide it to the public. The information is patchy and inconsistent, so we’re being transparent about what we find and how we handle it—the spreadsheet includes our live comments about changing data and how we’re working with incomplete information.

From here, you can also learn about our methodology, see who makes this, and find out what information states provide and how we handle it.

Supplementary data and videos for the manuscript 'Long-term live imaging, cell identification and cell tracking in regenerating crustacean legs', by Çevrim, Laplace-Builhé, Sugawara, Rusciano, Labert, Brocard, Almazán and Averof.

The supplementary data include:

Supplementary Data 1 (.csv file); Live imaging of regenerating Parhyale legs: image acquisition settings

Table with information on the 22 time lapse recordings presented in Figure 3, including image acquisition settings, temperature and duration of the recordings.

Supplementary Data 2 (.zip file); Live imaging of regenerated Parhyale legs: maximum projections

Compressed folder including maximum projections for each of the 22 time lapse recordings presented in Figure 3. These files were generated by projecting all or a subset of the z slices acquired at each time point. A 20 micron scale bar was added on the first time point. These files serve as a quick way to examine the 22 time lapse recordings.

Supplementary Data 3 (22 .tif files); Live imaging of regenerated Parhyale legs: complete datasets

Complete image 3D+T hyperstacks for each of the 22 time lapse recordings presented in Figure 3. These files have been generated by concatenating the original image stacks and correcting any image shifts, as described in the Methods section of the paper.

Supplementary Data 4 (.zip file); Analysis of trade-offs of imaging resolution and image quality

The data used for the analysis of trade-offs in imaging and the results shown in Table 1 are included in this compressed folder. Folders for the original recording (labelled 00), for each of the subsampled datasets (labelled 01 to 05), and for the denoised and deconvoluted datasets each include the corresponding image data and ground truth cell tracking files (.tif, .h5, .xml and .mastodon files) and three sets of cell track predictions (.mastodon files). There are also separate folders containing the Elephant detection and flow model parameters for each set of predictions.

Supplementary Data 4 (.zip file); Analysis of trade-offs of imaging resolution and image quality

The data used for the analysis of trade-offs in imaging and the results shown in Table 1 are included in two folders. The folder named Image_and_tracking_data includes the image data (.tif, .h5, .xml), ground truth cell tracking files (.mastodon files) and three sets of cell track predictions (.mastodon files) for the original recording (labelled 00), for each of the subsampled datasets (labelled 01 to 05), and for the denoised and deconvoluted datasets. It also includes separate folders containing the Elephant detection and flow model parameters for each set of predictions. The folder named CTC_tracking_results includes the ground-truth data along with three sets of predictions for detection and tracking for each dataset, following the Cell Tracking Challenge format. For each dataset we include label image files (.tif) for every time point along with tracking results in .txt format, and each results directory (01_RES_*) also contains the evaluation results from the Cell Tracking Challenge Evaluation Software. For a detailed explanation of the folder structure, please refer to the Cell Tracking Challenge documentation.

Supplementary Data 5 (.zip file); Tracking the progenitors of spineless-expressing cells in the distal carpus

The data used to generate Figure 6 are included in this compressed folder, including the live imaging and cell tracking files (.h5, .xml and .mastodon files) and the image stack of the spineless and futsch HCR and DAPI stainings (.tif file). Channel 2 shows spineless expression (mostly nascent transcripts in nuclei), as well as background signal in epidermal nuclei (possibly due to photoconversion of DAPI, see Karg & Golic 2018, Chromosoma 127: 235-245) and strong autofluorescence in granular cells (also visible in channel 1, depicting futsch HCR).

Supplementary Data 6 (.txt file); Sequences of Parhyale genes targeted by the HCR probes

The sequences are provided in FASTA format.

Supplementary Data 7 (.zip file); Apoptosis in legs that have not been subjected to live imaging

The data used to generate Suppl. Figure 3 are contained in this compressed folder, including 9 image stacks of T4 and T5 legs fixed and stained with DAPI 3 days post amputation (with apoptotic nuclei marked) and a .txt file containing the apoptotic cell counts.

Supplementary Data 8 (.zip file); Analysis of tracking performance in relation to imaging depth

The data used to generate Suppl. Figure 4 are contained in this compressed folder, including separate folders for the data extracted from the analysis of datasets #1 to #5. Each folder includes data from three replicates (batches 001 to 003), with .csv files listing the z location of nucleus centroids (in µm) for the nuclei that were incorrectly detected by Elephant – either as false positives (FP) or as false negatives (FN) – and the ground truth data (GT). The folder also includes an .xlsx file gathering all the relevant data and the measurements of precision and recall.

Supplementary Data 9 (.zip file); Detecting the temporal pattern of cell divisions in regenerating legs

The data used to generate Figure 4 are contained in this compressed folder, including the five image datasets (.tif, .h5, .xml), the detected cell divisions (.mastodon files), and an .xlxs file containing all the cell divisions counts and graphs.

Video 1. Time lapse recording of regeneration in a Parhyale T5 leg (dataset li48-t5)

Live imaging of nuclei labelled with H2B-mREFruby (maximum projection of z slices 3-10). Proximal parts of the leg are to the left and the amputation site is at the right of the frame. For annotations of different features please refer to Figure 2. Shortly after leg amputation (0 hpa) hemocytes adhere to the wound. By 16 hpa the wound has melanized. Up to ~32 hpa epithelial cells can be seen migrating and accumulating at the wound, below the melanized scab (Figure 2A,B). Around 31 hpa, the leg tissues become detached from the scab (Figure 2C). At 43 hpa, the carpus-propodus boundary first becomes visible, and thereafter many cells can be observed dividing at the distal part of the leg stump (Figure 2D). At 56 hpa, the propodus-dactylus boundary first becomes visible (Figure 2E). At later stages, tissues in more proximal parts of the leg retract, making space for the regenerating leg to grow (Figure 2F,G). After ~90 hpa cell proliferation there is less cell proliferation and cell movements, and the nuclear positions within the tissue become fixed. Scale bars, 20 µm.

Video 2. Time lapse recording of regeneration in a Parhyale T5 leg (dataset li36-t5)

Live imaging of nuclei labelled with H2B-mREFruby (maximum projection of z slices 3-15). Proximal parts of the leg are to the left and the amputation site is at the right of the frame. The sequence of events is similar to that described in Video 1, but the progression is slower: epithelial migration towards the wound is observed up to 40 hpa, tissues detach from the scab at 65 hpa, and the carpus-propodus and propodus-dactylus boundaries first become visible at 78 and 91 hpa. The tissues making up the carpus and propodus can be seen pulsating from 105 to 145 hpa. Scale bars, 20 µm.

This metadata document describes the data contained in the "rawData" folder of this data package. This data package contains all data collected by the Argos System from 28 satellite transmitters attached to Common murres on their breeding range in arctic and western Alaska, 1994-1996. Five data files are included in the "rawData" folder of this data package. Two data files (with identical content) contain the raw Argos DIAG (Diagnostic) data, one in the legacy verbose ASCII format and one in a tabular Comma Separate Value (CSV) format. Two other data files (with identical content) contain the raw Argos DS (Dispose) data, one in the legacy verbose ASCII format and one in a tabular CSV format. The fifth file, "deploymentAttributes", contains one record for each transmitter deployment in a CSV formatted table. The deployment attributes file contains information such as when the transmitter was attached to the animal, when tracking of a live animal ended, and a variety of variables de ...

Context

I wanted to find a better way to provide live traffic updates. We dont all have access to the data from traffic monitoring sensors or whatever gets uploaded from people's smart phones to Apple, Google etc plus I question how accurate the traffic congestion is on Google Maps or other apps. So I figured that since buses are also in the same traffic and many buses stream their GPS location and other data live, that would be an ideal source for traffic data. I investigated the data streams available from many bus companies around the world and found MTA in NYC to be very reliable.

Content

This dataset is from the NYC MTA buses data stream service. In roughly 10 minute increments the bus location, route, bus stop and more is included in each row. The scheduled arrival time from the bus schedule is also included, to give an indication of where the bus should be (how much behind schedule, or on time, or even ahead of schedule).

Acknowledgements

Data is recorded from the MTA SIRI Real Time data feed and the MTA GTFS Schedule data.

Inspiration

I want to see what exploratory & discovery people come up with from this data. Feel free to download this dataset for your own use however I would appreciate as many Kernals included on Kaggle as we can get.

Based on the interest this generates I plan to collect more data for subsequent months down the track.

This metadata document describes the data contained in the "rawData" folder of this data package. This data package contains all data collected by the Argos System from 17 satellite transmitters attached to Whooper Swans at a non-breeding site in Miyagi Prefecture, Japan, 2009. Five data files are included in the "rawData" folder of this data package. Two data files (with identical content) contain the raw Argos DIAG (Diagnostic) data, one in the legacy verbose ASCII format and one in a tabular Comma Separate Value (CSV) format. Two other data files (with identical content) contain the raw Argos DS (Dispose) data, one in the legacy verbose ASCII format and one in a tabular CSV format. The fifth file, "deploymentAttributes", contains one record for each transmitter deployment in a CSV formatted table. The deployment attributes file contains information such as when the transmitter was attached to the animal, when tracking of a live animal ended, and a variety of variables describing the animal and transmitter. This table is identical to the "deploymentAttributes" table in the "processedData" folder of this data package.

The global market for Live Bus Query Software is experiencing robust growth, driven by increasing urbanization, rising smartphone penetration, and the expanding adoption of smart city initiatives. The convenience and real-time information provided by these applications are transforming public transportation experiences for commuters, leading to improved efficiency and reduced travel times. While precise market sizing data is unavailable, a reasonable estimation based on similar software-as-a-service (SaaS) markets and considering a conservative Compound Annual Growth Rate (CAGR) of 15% from a 2025 base of $500 million, projects a market value exceeding $1 billion by 2033. Key growth drivers include integration with other smart city platforms, enhanced data analytics capabilities providing valuable insights for transit agencies, and the increasing demand for user-friendly interfaces with features like real-time tracking, route planning, and service alerts. The market is segmented by deployment type (cloud-based and on-premise), application (mobile and web), and geography. Competition is fierce, with both established players like Moovit and smaller regional providers vying for market share. Challenges include ensuring data accuracy, maintaining system reliability across diverse network conditions, and addressing concerns about data privacy and security. Further growth depends on the successful implementation of large-scale smart city projects that leverage live bus query software as a crucial component. Continued innovation in areas such as AI-powered predictive analytics for optimizing routes and improving service reliability will be crucial. The adoption of advanced technologies like 5G networks will further enhance the real-time capabilities of these systems. Furthermore, addressing regulatory hurdles and ensuring interoperability between various transit systems and apps are vital for sustained market growth. Companies focusing on integration, customization, and superior customer support are better positioned to capitalize on the expanding opportunities in this dynamic market.

As a part of the effort to promote e-governance and judicial transparency, China has been promoting mass online digitalization of court, including an archive of judgment text (司法文书), a platform of online trial videos and live broadcasting (庭审直播), and a judgment implementation tracker (判决执行). China has become one of the few countries that allow cameras in the courtroom. Though a growing number of studies use court decision data, little research has been conducted on the court trial videos. The goal of the Chinese Courtroom Video Database is to meet the needs of those interested in broad research of government policy diffusion, judicial transparency, and judicial behavior in the China context by filling the vacancy in judicial data and providing a new perspective to the existing scholarship. The database includes two sets of data. The first dataset is a catalog of half-million entries of criminal and administrative trial videos in all 31 provinces from January 2013 to February 2019. Each profile records the basic information of a trial video, such as case identification number, date and time of the trial, participants, reason of trial, location of the court, number of views of the video, and other descriptions. The second dataset is a collection of 1,491 audio files of online criminal trials in Yunnan, China. Each audio was downloaded and converted from the original video. The datasets were collected using the Selenium package of Python and Downie, an online stream downloader.

This metadata document describes the data contained in the "rawData" folder of this data package. This data package contains all data collected by the Argos System from 53 satellite transmitters attached to Emperor geese on their breeding range in western Alaska, 1999-2003. Five data files are included in the "rawData" folder of this data package. Two data files (with identical content) contain the raw Argos DIAG (Diagnostic) data, one in the legacy verbose ASCII format and one in a tabular Comma Separate Value (CSV) format. Two other data files (with identical content) contain the raw Argos DS (Dispose) data, one in the legacy verbose ASCII format and one in a tabular CSV format. The fifth file, "deploymentAttributes", contains one record for each transmitter deployment in a CSV formatted table. The deployment attributes file contains information such as when the transmitter was attached to the animal, when tracking of a live animal ended, and a variety of variables describing the animal and transmitter. This table is identical to the "deploymentAttributes" table in the "processedData" folder of this data package.

Large-scale Corynebacterium glutamicum data set with Segmentation and Tracking Annotation

We provide five time-lapse sequences with manually corrected segmentation and tracking annotations of growing C. glutamicum cultivations. The dataset contains more than 1.4 million cell observations in 29k cell tracks and 14k cell divisions. We provide videos of the annotations (videos.zip) and the dataset in Cell Tracking Challenge format (ctc_format.zip). In the videos, cell contours are rendered in yellow, cell links between frames are colored red and cell divisions, and their links are colored in blue.

Data Acquisition

Corynebacterium glutamicum ATCC 13032 was cultivated in BHI-medium at 30°C in this study. From and overnight preculture, the main culture was inoculated the next day with a starting OD600 of 0.05 and grown at 120 rpm to a OD600 of 0.25. A chip was fabricated, according to (Täuber et al., 2020), and fixed to the microscope’s holder. The main culture cells were transferred to monolayer growth chambers (height = 720 nm) on the microfluidic chip. Flow through the microfluidic device was mediated by pressure driven pumps with a pressure of 100 mbar on the medium reservoir.

The time-lapse phase contrast images of five monolayer growth chambers were taken every minute using an inverted microscope (Nikon Eclipse Ti2) with a 100x oil emersion objective and a DS-QI2 camera (Nikon) at 15 % relative DIA-illumination intensity and 100 ms exposure time. The spatial image resolution is 0.072 μm/px.

The data collected by Spire from it's 100 satellites launched into Low Earth Orbit (LEO) has a diverse range of applications, from analysis of global trade patterns and commodity flows to aircraft routing to weather forecasting. The data also provides interesting research opportunities on topics as varied as ocean currents and GNSS-based planetary boundary layer height. The following products can be requested:

GNSS Polarimetric Radio Occultation (STRATOS) Novel Polarimetric Radio Occultation (PRO) measurements collected by three Spire satellites are available over 15-May-2023 to 30-November-2023. PRO differ from regular RO (described below) in that the H and V polarizations of the signal are available, as opposed to only Right-Handed Circularly Polarized (RHCP) signals in regular RO. The differential phase shift between H and V correlates with the presence of hydrometeors (ice crystals, rain, snow, etc.). When combined, the H and V information provides the same information on atmospheric thermodynamic properties as RO: temperature, humidity, and pressure, based on the signal’s bending angle. Various levels of the products are provided.

GNSS Reflectometry (STRATOS) GNSS Reflectometry (GNSS-R) is a technique to measure Earth’s surface properties using reflections of GNSS signals in the form of a bistatic radar. Spire collects two types of GNSS-R data: Near-Nadir incidence LHCP reflections collected by the Spire GNSS-R satellites, and Grazing-Angle GNSS-R (i.e., low elevation angle) RHCP reflections collected by the Spire GNSS-RO satellites. The Near-Nadir GNSS-R collects DDM (Delay Doppler Map) reflectivity measurements. These are used to compute ocean wind / wave conditions and soil moisture over land. The Grazing-Angle GNSS-R collects 50 Hz reflectivity and additionally carrier phase observations. These are used for altimetry and characterization of smooth surfaces (such as ice and inland water). Derived Level 1 and Level 2 products are available, as well as some special Level 0 raw intermediate frequency (IF) data. Historical grazing angle GNSS-R data are available from May 2019 to the present, while near-nadir GNSS-R data are available from December 2020 to the present.

Name Temporal coverage Spatial coverage Description Data format and content Application Polarimetric Radio Occultation (PRO) measurements 15-May-2023 to 30-November-2023 Global PRO measurements observe the properties of GNSS signals as they pass through by Earth's atmosphere, similar to regular RO measurements. The polarization state of the signals is recorded separately for H and V polarizations to provide information on the anisotropy of hydrometeors along the propagation path. leoOrb.sp3. This file contains the estimated position, velocity and receiver clock error of a given Spire satellite after processing of the POD observation file PRO measurements add a sensitivity to ice and precipitation content alongside the traditional RO measurements of the atmospheric temperature, pressure, and water vapor. proObs. Level 0 - Raw open loop carrier phase measurements at 50 Hz sampling for both linear polarization components (horizontal and vertical) of the occulted GNSS signal. h(v)(c)atmPhs. Level 1B - Atmospheric excess phase delay computed for each individual linear polarization component (hatmPhs, vatmPhs) and for the combined (“H” + “V”) signal (catmPhs). Also contains values for signal-to-noise ratio, transmitter and receiver positions and open loop model information.
polPhs. Level 1C - Combines the information from the hatmPhs and vatmPhs files while removing phase continuities due to phase wrapping and navigation bit modulation.
patmPrf. Level 2 - Bending angle, dry refractivity, and dry temperature as a function of mean sea level altitude and impact parameter derived from the “combined” excess phase delay (catmPhs)
Near-Nadir GNSS Reflectometry (NN GNSS-R) measurements 25-January-2024 to 24-July-2024 Global Tracks of surface reflections as observed by the near-nadir pointing GNSS-R antennas, based on Delay Doppler Maps (DDMs). gbrRCS.nc. Level 1B - Along-track calibrated bistatic radar cross-sections measured by Spire conventional GNSS-R satellites. NN GNSS-R measurements are used to measure ocean surface winds and characterize land surfaces for applications such as soil moisture, freeze/thaw monitoring, flooding detection, inland water body delineation, sea ice classification, etc. gbrNRCS.nc. Level 1B - Along-track calibrated bistatic and normalized radar cross-sections measured by Spire conventional GNSS-R satellites.
gbrSSM.nc. Level 2 - Along-track SNR, reflectivity, and retrievals of soil moisture (and associated uncertainties) and probability of frozen ground.
gbrOcn.nc. Level 2 - Along-track retrievals of mean square slope (MSS) of the sea surface, wind speed, sigma0, and associated uncertainties.
Grazing angle GNSS Reflectometry (GA GNSS-R) measurements 25-January-2024 to 24-July-2024 Global Tracks of surface reflections as observed by the limb-facing RO antennas, based on open-loop tracking outputs: 50 Hz collections of accumulated I/Q observations. grzRfl.nc. Level 1B - Along-track SNR, reflectivity, phase delay (with respect to an open loop model) and low-level observables and bistatic radar geometries such as receiver, specular reflection, and the transmitter locations. GA GNSS-R measurements are used to 1) characterize land surfaces for applications such as sea ice classification, freeze/thaw monitoring, inland water body detection and delineation, etc., and 2) measure relative altimetry with dm-level precision for inland water bodies, river slopes, sea ice freeboard, etc., but also water vapor characterization from delay based on tropospheric delays. grzIce.nc. Level 2 - Along-track water vs sea ice classification, along with sea ice type classification.
grzAlt.nc. Level 2 - Along-track phase-delay, ionosphere-corrected altimetry, tropospheric delay, and ancillary models (mean sea surface, tides).

Additionally, the following products (better detailed in the ToA) can be requested but the acceptance is not guaranteed and shall be evaluated on a case-by-case basis: Other STRATOS measurements: profiles of the Earth’s atmosphere and ionosphere, from December 2018 ADS-B Data Stream: monthly subscription to global ADS-B satellite data, available from December 2018 AIS messages: AIS messages observed from Spire satellites (S-AIS) and terrestrial from partner sensor stations (T-AIS), monthly subscription available from June 2016

The products are available as part of the Spire provision with worldwide coverage. All details about the data provision, data access conditions and quota assignment procedure are described in the _\(Terms of Applicability\) https://earth.esa.int/eogateway/documents/20142/37627/SPIRE-Terms-Of-Applicability.pdf/0dd8b3e8-05fe-3312-6471-a417c6503639 .

Live monthly tracker of LLMS.txt adoption across the top 1,000 global websites, featuring in-depth research, real-time data, and analysis of AI transparency standards.

This layer features tropical storm (hurricanes, typhoons, cyclones) tracks, positions, and observed wind swaths from the past hurricane season for the Atlantic, Pacific, and Indian Basins. These are products from the National Hurricane Center (NHC) and Joint Typhoon Warning Center (JTWC). They are part of an archive of tropical storm data maintained in the International Best Track Archive for Climate Stewardship (IBTrACS) database by the NOAA National Centers for Environmental Information.Data SourceNOAA National Hurricane Center tropical cyclone best track archive.Update FrequencyWe automatically check these products for updates every 15 minutes from the NHC GIS Data page.The NHC shapefiles are parsed using the Aggregated Live Feeds methodology to take the returned information and serve the data through ArcGIS Server as a map service.Area CoveredWorldWhat can you do with this layer?Customize the display of each attribute by using the ‘Change Style’ option for any layer.Run a filter to query the layer and display only specific types of storms or areas.Add to your map with other weather data layers to provide insight on hazardous weather events.Use ArcGIS Online analysis tools like ‘Enrich Data’ on the Observed Wind Swath layer to determine the impact of cyclone events on populations.Visualize data in ArcGIS Insights or Operations Dashboards.This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency. Always refer to NOAA or JTWC sources for official guidance.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page

Our extensive database contains approximately 800,000 active rental property listings from across the United States. Updated daily, this comprehensive collection provides real estate professionals, investors, and property managers with valuable market intelligence and business opportunities. Database Contents

Property Addresses: Complete location data including street address, city, state, ZIP code Listing Dates: Original listing date and most recent update date Availability Status: Currently available, pending, or recently rented properties Geographic Coverage: Properties spanning all 50 states and major metropolitan areas

Applications & Uses

Market Analysis: Track rental pricing trends across different regions and property types Investment Research: Identify high-opportunity markets with favorable rental conditions Lead Generation: Connect with property owners potentially needing management services Competitive Intelligence: Monitor listing volumes, vacancy rates, and market saturation Business Development: Target specific neighborhoods or property categories for expansion

File Format & Delivery

Organized in easy-to-use CSV format for seamless integration with data analysis tools Accessible through secure download portal or API connection Daily updates ensure you're working with the most current market information Custom filtering options available to narrow results by location, date range, or other criteria

Data Quality

Rigorous validation processes to ensure address accuracy Duplicate listing detection and removal Regular verification of active status Standardized format for consistent analysis

Subscription Benefits

Access to historical listing archives for trend analysis Advanced search capabilities to target specific property characteristics Regular market reports summarizing key trends and opportunities Custom data exports tailored to your specific business needs

AK ~ 1,342 listings AL ~ 6,636 listings AR ~ 4,024 listings AZ ~ 25,782 listings CA ~ 102,833 listings CO ~ 14,333 listings CT ~ 10,515 listings DC ~ 1,988 listings DE ~ 1,528 listings FL ~ 152,258 listings GA ~ 28,248 listings HI ~ 3,447 listings IA ~ 4,557 listings ID ~ 3,426 listings IL ~ 42,642 listings IN ~ 8,634 listings KS ~ 3,263 listings KY ~ 5,166 listings LA ~ 11,522 listings MA ~ 53,624 listings MD ~ 12,124 listings ME ~ 1,754 listings MI ~ 12,040 listings MN ~ 7,242 listings MO ~ 10,766 listings MS ~ 2,633 listings MT ~ 1,953 listings NC ~ 22,708 listings ND ~ 1,268 listings NE ~ 1,847 listings NH ~ 2,672 listings NJ ~ 31,286 listings NM ~ 2,084 listings NV ~ 13,111 listings NY ~ 94,790 listings OH ~ 15,843 listings OK ~ 5,676 listings OR ~ 8,086 listings PA ~ 37,701 listings RI ~ 4,345 listings SC ~ 8,018 listings SD ~ 1,018 listings TN ~ 15,983 listings TX ~ 132,620 listings UT ~ 3,798 listings VA ~ 14,087 listings VT ~ 946 listings WA ~ 15,039 listings WI ~ 7,393 listings WV ~ 1,681 listings WY ~ 730 listings

Grand Total ~ 977,010 listings