Updates are delayed due to technical difficulties. How many people are staying at home? How far are people traveling when they don’t stay home? Which states and counties have more people taking trips? The Bureau of Transportation Statistics (BTS) now provides answers to those questions through our new mobility statistics. The Trips by Distance data and number of people staying home and not staying home are estimated for the Bureau of Transportation Statistics by the Maryland Transportation Institute and Center for Advanced Transportation Technology Laboratory at the University of Maryland. The travel statistics are produced from an anonymized national panel of mobile device data from multiple sources. All data sources used in the creation of the metrics contain no personal information. Data analysis is conducted at the aggregate national, state, and county levels. A weighting procedure expands the sample of millions of mobile devices, so the results are representative of the entire population in a nation, state, or county. To assure confidentiality and support data quality, no data are reported for a county if it has fewer than 50 devices in the sample on any given day. Trips are defined as movements that include a stay of longer than 10 minutes at an anonymized _location away from home. Home locations are imputed on a weekly basis. A movement with multiple stays of longer than 10 minutes before returning home is counted as multiple trips. Trips capture travel by all modes of transportation. including driving, rail, transit, and air. The daily travel estimates are from a mobile device data panel from merged multiple data sources that address the geographic and temporal sample variation issues often observed in a single data source. The merged data panel only includes mobile devices whose anonymized _location data meet a set of data quality standards, which further ensures the overall data quality and consistency. The data quality standards consider both temporal frequency and spatial accuracy of anonymized _location point observations, temporal coverage and representativeness at the device level, spatial representativeness at the sample and county level, etc. A multi-level weighting method that employs both device and trip-level weights expands the sample to the underlying population at the county and state levels, before travel statistics are computed. These data are experimental and may not meet all of our quality standards. Experimental data products are created using new data sources or methodologies that benefit data users in the absence of other relevant products. We are seeking feedback from data users and stakeholders on the quality and usefulness of these new products. Experimental data products that meet our quality standards and demonstrate sufficient user demand may enter regular production if resources permit.

Scenario 2 average between distance and relative standard deviation for communities simulated with and .

The Network Adequacy Standards data is divided out by Provider Type, Adult and Pediatric separately, so that the Time or Distance analysis can be performed with greater detail. These standards differ by County due to the County "type" which is based on the population density of the county. There are 5 county categories within California; Rural (<50 people/sq mile), Small (51-200 people/sq mile), Medium (201-599 people/sq mile), and Dense (>600 people/sq mile).HospitalsOB/GYN SpecialtyAdult Cardiology/Interventional CardiologyAdult DermatologyAdult EndocrinologyAdult ENT/OtolaryngologyAdult GastroenterologyAdult General SurgeryAdult HematologyAdult HIV/AIDS/Infectious DiseaseAdult Mental Health Outpatient ServicesAdult NephrologyAdult NeurologyAdult OncologyAdult OphthalmologyAdult Orthopedic SurgeryAdult PCPAdult Physical Medicine and RehabilitationAdult PsychiatryAdult PulmonologyPediatric Cardiology/Interventional CardiologyPediatric DermatologyPediatric EndocrinologyPediatric ENT/OtolaryngologyPediatric GastroenterologyPediatric General SurgeryPediatric HematologyPediatric HIV/AIDS/Infectious DiseasePediatric Mental Health Outpatient ServicesPediatric NephrologyPediatric NeurologyPediatric OncologyPediatric OphthalmologyPediatric Orthopedic SurgeryPediatric PCPPediatric Physical Medicine and RehabilitationPediatric PsychiatryPediatric Pulmonology

The shortest route is not always the most time-efficient, the fastest route not always the most cost-effective. The PTV Entfernungswerk Straße (EWS) distance tables has been the basis of calculations for transport services since the discontinuation of the GFT (long-distance freight tariff) and has established itself as a quasi-standard in the industry.

This dataset tracks the updates made on the dataset "Managed Care Network Adequacy - Time/Distance Standards by County" as a repository for previous versions of the data and metadata.

Data in support of the paper "Improving Efficiency Through the Publication of Expected Distances for Standard Terminal Arrival Routes". The data is organised in subfolders containing the data for each of the three analysed airports (LSGG, EDDM, LIRF).

Each folder includes a landing_full.parquet traffic file that contains all analyzed trajectories for the respective airport, along with random subsamples of sizes 10,000, 5,000, and 2,500.

Additionally, each folder also contains samples of 1000 trajectories following each of the four specific STAR procedure that were analysed per airport:

LSGG: AKITO 3R, BELUS 3N, KINES 2N, LUSAR 2N

EDDM: BETOS 1A, LANDU 1B, NAPSA 1B, ROKIL 1A

LIRF: ELKAP 2A, LAT 2C, RITEB 2A, VALMA 2C

Finally, each folder also contains a landing_df.parquet file that summarises the following key information about each of the trajectories contained in the landing_full.parquet file:

ID A unique identifier linking the row to the corresponding trajectory data

Typecode The ICAO typecode of the aircraft

Start Timestamp when the aircraft passes the first waypoint of the STAR

Stop Timestamp when the aircraft crosses the runway threshold

Runway Designator of the landing runway

STAR Name of the STAR procedure used by the aircraft

Distance Total distance traveled by the aircraft from the initial waypoint of the STAR to the runway threshold

How many people are staying at home? How far are people traveling when they don’t stay home? Which states and counties have more people taking trips? The Bureau of Transportation Statistics (BTS) now provides answers to those questions through our new mobility statistics. The Trips by Distance data and number of people staying home and not staying home are estimated for the Bureau of Transportation Statistics by the Maryland Transportation Institute and Center for Advanced Transportation Technology Laboratory at the University of Maryland. The travel statistics are produced from an anonymized national panel of mobile device data from multiple sources. All data sources used in the creation of the metrics contain no personal information. Data analysis is conducted at the aggregate national, state, and county levels. A weighting procedure expands the sample of millions of mobile devices, so the results are representative of the entire population in a nation, state, or county. To assure confidentiality and support data quality, no data are reported for a county if it has fewer than 50 devices in the sample on any given day. Trips are defined as movements that include a stay of longer than 10 minutes at an anonymized location away from home. Home locations are imputed on a weekly basis. A movement with multiple stays of longer than 10 minutes before returning home is counted as multiple trips. Trips capture travel by all modes of transportation. including driving, rail, transit, and air. The daily travel estimates are from a mobile device data panel from merged multiple data sources that address the geographic and temporal sample variation issues often observed in a single data source. The merged data panel only includes mobile devices whose anonymized location data meet a set of data quality standards, which further ensures the overall data quality and consistency. The data quality standards consider both temporal frequency and spatial accuracy of anonymized location point observations, temporal coverage and representativeness at the device level, spatial representativeness at the sample and county level, etc. A multi-level weighting method that employs both device and trip-level weights expands the sample to the underlying population at the county and state levels, before travel statistics are computed. These data are experimental and may not meet all of our quality standards. Experimental data products are created using new data sources or methodologies that benefit data users in the absence of other relevant products. We are seeking feedback from data users and stakeholders on the quality and usefulness of these new products. Experimental data products that meet our quality standards and demonstrate sufficient user demand may enter regular production if resources permit.

Reflector height, the distance between the receiver antenna phase center and ground surface, are derived from the SNR data using GPS-IR.The data are stored in the text format with four column (year , day of year, reflector height, uncertainty) year by year. The reflector heights span from year 2007 to 2016, whose changes are opposite to the changes of ground surface.

This dataset presents a comparative analysis between a high accurate reference point cloud acquired using the Leica ATR 960 (Laser tracker) and Leica LAS XL (Hand-held scanner), and a total of 51 laser scans point clouds using Z+F Imager 5016. The comparisons were carried out at the Hitec Laboratory of the Geodetic Institute Hannover, where controlled scanning conditions were maintained while capturing various objects.

Throughout the entire measurement process, great care was taken to ensure constant temperature and air pressure. The deviations observed through backward modeling are reflected in the distance measurements. Additionally, to explore potential factors influencing TLS distance measurements, feature engineering was conducted. The dataset is exceptionally well-suited for understanding and potentially modeling the uncertainties associated with TLS distance measurements.

Measurement process and backward modelling

https://data.uni-hannover.de/dataset/e0dd7c6c-de06-4c44-8848-e1d7f9757a1a/resource/93a1a7a0-0704-406c-a58b-0d0181cbe6ec/download/measurement_process.jpg" alt="">

Feature engineering

The formulas used for feature engineering are displayed in the following document: Feature engineering

Object describtion & Viewpoints

The definitions of individual objects can be extracted from the following figures. It can be observed that some objects exhibit similar characteristics. https://data.uni-hannover.de/dataset/e0dd7c6c-de06-4c44-8848-e1d7f9757a1a/resource/4a305c9d-00db-4107-82d6-e58dafb37ada/download/objects.jpg" alt="Objects inside the Hitec Laboratory">

The TLS viewpoints were distributed throughout the entire space of the laboratory. The 3D coordinates of the viewpoints as well as the corresponding standard deviations of the translation parameters, derived from the georeferencing process are given in document. Viewpoint overview

Moreover, it should be mentioned that some TLS viewpoints have duplicate scans taken in the first and second phase.

https://data.uni-hannover.de/dataset/e0dd7c6c-de06-4c44-8848-e1d7f9757a1a/resource/af7eb4e9-fb96-43c2-b3a6-00d0bcd3cbc6/download/environment.jpg" alt="">

Data set description

Each object in the dataset has its own individual data stored as a PLY file. These PLY files contain not only the XYZ coordinates but also the features and residuals. A comprehensive description of the dataset can be found in the associated documentation. Data description

This dataset is sourced from the U.S. Department of Transportation Bureau of Transportation Statistics. All data and metadata is sourced from the page linked below. Metadata is not updated automatically; data updates weekly.

Source Data Link: https://data.bts.gov/Research-and-Statistics/Trips-by-Distance/w96p-f2qv

How many people are staying at home? How far are people traveling when they don’t stay home? Which states and counties have more people taking trips? The Bureau of Transportation Statistics (BTS) now provides answers to those questions through our new mobility statistics.

The Trips by Distance data and number of people staying home and not staying home are estimated for the Bureau of Transportation Statistics by the Maryland Transportation Institute and Center for Advanced Transportation Technology Laboratory at the University of Maryland. The travel statistics are produced from an anonymized national panel of mobile device data from multiple sources. All data sources used in the creation of the metrics contain no personal information. Data analysis is conducted at the aggregate national, state, and county levels. A weighting procedure expands the sample of millions of mobile devices, so the results are representative of the entire population in a nation, state, or county. To assure confidentiality and support data quality, no data are reported for a county if it has fewer than 50 devices in the sample on any given day.

Trips are defined as movements that include a stay of longer than 10 minutes at an anonymized location away from home. Home locations are imputed on a weekly basis. A movement with multiple stays of longer than 10 minutes before returning home is counted as multiple trips. Trips capture travel by all modes of transportation. including driving, rail, transit, and air.

The daily travel estimates are from a mobile device data panel from merged multiple data sources that address the geographic and temporal sample variation issues often observed in a single data source. The merged data panel only includes mobile devices whose anonymized location data meet a set of data quality standards, which further ensures the overall data quality and consistency. The data quality standards consider both temporal frequency and spatial accuracy of anonymized location point observations, temporal coverage and representativeness at the device level, spatial representativeness at the sample and county level, etc. A multi-level weighting method that employs both device and trip-level weights expands the sample to the underlying population at the county and state levels, before travel statistics are computed.

These data are experimental and may not meet all of our quality standards. Experimental data products are created using new data sources or methodologies that benefit data users in the absence of other relevant products. We are seeking feedback from data users and stakeholders on the quality and usefulness of these new products. Experimental data products that meet our quality standards and demonstrate sufficient user demand may enter regular production if resources permit.

This dataset tracks the updates made on the dataset "Managed Care Network Adequacy - Population Points with Time/Distance Standards" as a repository for previous versions of the data and metadata.

Average distance between the NetSel extracted communities and relative standard deviation.

Introduction

This data set contains two collections of phase angle measurements created in two indoor and one outdoor environment that can be used for phase-based distance estimates. The measurements include phase samples created on two different frequency sets:

TSCH standard frequencies: measurements are performed on default 16 channel frequencies {2405.0, 2410.0, 2415.0, 2420.0, 2425.0, 2430.0, 2435.0, 2440.0, 2445.0, 2450.0, 2455.0, 2460.0, 2465.0, 2470.0, 2474.0, 2480.0}MHz.

Golomb ruler frequencies: the measurements are performed on 15 custom selected frequencies according to the Golomb ruler technique {2400.5, 2406.0, 2407.5, 2408.0, 2412.5, 2423.0, 2431.0, 2442.5, 2452.0, 2460.5, 2463.0, 2466.5, 2476.5, 2479.5, 2480.5}MHz.

Measurement setup

Measurements were performed using AT86RF233 transceivers connected to the in-house VESNA platform. Two nodes were placed on a stand 1.6 m above the ground in three separate environments:

in a 5x5m square office with no furniture

in an indoor hallway with dimensions of 4x40m

in a park without any nearby obstacles

The actual distance between nodes was measured with a laser ranger with an accuracy of ±1.5 mm. There was no obstacle between the devices. Indoors, 17 WiFi access points were in operation during the measurement campaign.

Phase measurement process

The devices involved first establish an IEEE 802.15.4 TSCH network. In it, they measure the phase difference on pre-selected frequencies. The phase measurement has been seamlessly integrated into a communication so that the devices obtain phase measurement with every packet sent.

Why two collections?

The set labelled "TSCH standard channels" contains phase measurements created at frequencies defined in the IEEE.802.15.4 standard for the 2.4 GHz band. The frequency step ((\Delta freq = freq_{i+1} - freq_{i})) between two phase samples is equal to 5MHz, which results in a maximum distinguishable range of 30m for the distance estimation.

To increase the range up to 300m, the frequency step must be reduced to 0.5 MHz. This requires 160 phase samples in the 2.4 GHz band used with a bandwidth of 80 MHz. However, measuring 160 phase samples on 160 frequencies would take a lot of time and therefore interfere with TSCH communications. One way to shorten the procedure is to use the Golomb ruler technique. This allows a large set of phase differences to be created from a small number of measured phases. This method was used in the creation of the set named "Golomb ruler frequencies". The data set also contains a Python example script that expands the set of 15 measured frequencies to a set of 160 samples.

Folder structure

Each record collection is stored in a corresponding folder. Each folder contains .json files representing different environments. In addition to the data sets, the folders also contain figures and a sample Python script. The measurements are stored in JSON format. Each measured distance contains the number of measurements and the actual data. With each packet sent (identified by its Absolute Slot Number (ASN)), the phase difference between the devices is measured. The phase value is stored as an 8-bit value representing the range from 0 to 2 pi.

Standard station location and cast distance tables

​CFIA observations of insect and plant species in Canada used for the validation of the standardized Mahalanobis distance species distribution model. Co-ordinates are rounded to a single decimal place.

Glaciers flowing into the Amundsen Sea Embayment (ASE) account for > 35% of the total discharge of the West Antarctic Ice Sheet (WAIS) and have thinned and retreated dramatically over the past two decades. Here we present detailed marine geological data and an extensive new radiocarbon dataset from the eastern ASE in order to constrain the retreat of the WAIS since the Last Glacial Maximum (LGM) and assess the significance of these recent changes. Our dating approach, relying mainly on the acid insoluble organic (AIO) fraction, utilises multi-proxy analyses of the sediments to characterise their lithofacies and determine the horizon in each core that would yield the most reliable age for deglaciation. In total, we dated 69 samples and show that deglaciation of the outer shelf was underway before 20,600 calibrated years before present (cal. yr BP), reaching the mid-shelf by 13,575 cal. yr BP and the inner shelf to within c.150 km of the present grounding line by 10,615 cal. yr BP. The timing of retreat is broadly consistent with previously published radiocarbon dates on biogenic carbonate from the eastern ASE as well as AIO 14C ages from the western ASE and provides new constraints for ice sheet models. The overall retreat trajectory - slow on the outer shelf, more rapid from the middle to inner shelf - clearly highlights the importance of reverse bedslopes in controlling phases of accelerated groundling line retreat. Despite revealing these broad scale trends, the current dataset does not capture detailed changes in ice flow, such as stillstands during grounding line retreat (i.e., deposition of grounding zone wedges) and possible readvances as depicted in the geomorphological record.

Students will discover how interstate highways affect the distance traveled in a given amount of time. Differences between drive time distances and buffers is alo explored.Educational standards addressed:APHG.I.C.5. Use concepts like space, place, and region to examine geographic issues. APHG.I.C.6. Interpret patterns and processes at different scales.

Scenario 1A average between distance and relative standard deviation for communities simulated with and .

The genetic distance between biological sequences is a fundamental quantity in molecular evolution. It pertains to questions of rates of evolution, existence of a molecular clock, and phylogenetic inference. Under the class of continuous-time substitution models, the distance is commonly defined as the expected number of substitutions at any site in the sequence. We eschew the almost ubiquitous assumptions of evolution under stationarity and time-reversible conditions and extend the concept of the expected number of substitutions to non-stationary Markov models where the only remaining constraint is of time homogeneity between nodes in the tree. Our measure of genetic distance reduces to the standard formulation if the data in question are consistent with the stationarity assumption. We apply this general model to samples from across the tree of life to compare distances so obtained with those from the general time-reversible model, with and without rate heterogeneity across sites, and the paralinear distance, an empirical pairwise method explicitly designed to address non-stationarity. We discover that estimates from both variants of the general time-reversible model and the paralinear distance systematically overestimate genetic distance and departure from the molecular clock. The magnitude of the distance bias is proportional to departure from stationarity, which we demonstrate to be associated with longer edge lengths. The marked improvement in consistency between the general non-stationary Markov model and sequence alignments leads us to conclude that analyses of evolutionary rates and phylogenies will be substantively improved by application of this model.

Layers D to A layers adjacent to layer E going increasingly deeper into the crust as indicated in Fig 2.