The United States recorded a trade deficit of 61.62 USD Billion in April of 2025. This dataset provides the latest reported value for - United States Balance of Trade - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The United States' total Imports in 2024 were valued at US$3.36 Trillion, according to the United Nations COMTRADE database on international trade. The United States' main import partners were: Mexico, China and Canada. The top three import commodities were: Machinery, nuclear reactors, boilers; Electrical, electronic equipment and Vehicles other than railway, tramway. Total Exports were valued at US$2.06 Trillion. In 2024, The United States had a trade deficit of US$1.29 Trillion.

Imports in the United States decreased to 350.99 USD Billion in April from 419.39 USD Billion in March of 2025. This dataset provides the latest reported value for - United States Imports - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

U.S. tariff rates for 2022 was 1.49%, a 0.02% increase from 2021.

<ul style='margin-top:20px;'>

<li>U.S. tariff rates for 2021 was <strong>1.47%</strong>, a <strong>0.05% decline</strong> from 2020.</li>
<li>U.S. tariff rates for 2020 was <strong>1.52%</strong>, a <strong>12.26% decline</strong> from 2019.</li>
<li>U.S. tariff rates for 2019 was <strong>13.78%</strong>, a <strong>12.19% increase</strong> from 2018.</li>
</ul>Weighted mean applied tariff is the average of effectively applied rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of weighted mean tariffs. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead.

Exports in the United States increased to 289.37 USD Billion in April from 281.07 USD Billion in March of 2025. This dataset provides the latest reported value for - United States Exports - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The United States' total Exports in 2024 were valued at US$2.06 Trillion, according to the United Nations COMTRADE database on international trade. The United States' main export partners were: Canada, Mexico and China. The top three export commodities were: Mineral fuels, oils, distillation products; Machinery, nuclear reactors, boilers and Electrical, electronic equipment. Total Imports were valued at US$3.36 Trillion. In 2024, The United States had a trade deficit of US$1.29 Trillion.

A joint venture involving the National Atlas programs in Canada (Natural Resources Canada), Mexico (Instituto Nacional de Estadística Geografía e Informática), and the United States (U.S. Geological Survey), as well as the North American Commission for Environmental Co-operation, has led to the release (June 2004) of several new products: an updated paper map of North America, and its associated geospatial data sets and their metadata. These data sets are available online from each of the partner countries both for visualization and download. The North American Atlas data are standardized geospatial data sets at 1:10,000,000 scale. A variety of basic data layers (e.g. roads, railroads, populated places, political boundaries, hydrography, bathymetry, sea ice and glaciers) have been integrated so that their relative positions are correct. This collection of data sets forms a base with which other North American thematic data may be integrated. Any data outside of Canada, Mexico, and the United States of America included in the North American Atlas data sets is strictly to complete the context of the data. The North American Atlas - Populated Places data set shows a selection of named populated places suitable for use at a scale of 1:10,000,000. Places, which refer to individual municipalities, are always shown using point symbols. These symbols have been fitted to the North American Atlas roads, railroads, and hydrography layers, so that the points represent the approximate locations of places relative to data in these other layers. The selection of populated places was based on local importance (as shown by population size), importance as a cross-border point, and, occasionally, on other factors. All capital cities (national, provincial, territorial or State) are shown for Canada, Mexico, and the United States of America. Attributes were added to the data to reflect population class, name, and capital. Cartographic considerations were taken into account so that names do not overlap in crowded areas, nor are there too many names shown for sparsely-populated areas.

Dataset Description

This dataset is a large-scale set of measurements for RSS-based localization. The data consists of received signal strength (RSS) measurements taken using the POWDER Testbed at the University of Utah. Samples include either 0, 1, or 2 active transmitters.

The dataset consists of 5,214 unique samples, with transmitters in 5,514 unique locations. The majority of the samples contain only 1 transmitter, but there are small sets of samples with 0 or 2 active transmitters, as shown below. Each sample has RSS values from between 10 and 25 receivers. The majority of the receivers are stationary endpoints fixed on the side of buildings, on rooftop towers, or on free-standing poles. A small set of receivers are located on shuttles which travel specific routes throughout campus.

Dataset Description	Sample Count	Receiver Count
No-Tx Samples	46	10 to 25
1-Tx Samples	4822	10 to 25
2-Tx Samples	346	11 to 12

The transmitters for this dataset are handheld walkie-talkies (Baofeng BF-F8HP) transmitting in the FRS/GMRS band at 462.7 MHz. These devices have a rated transmission power of 1 W. The raw IQ samples were processed through a 6 kHz bandpass filter to remove neighboring transmissions, and the RSS value was calculated as follows:

\(RSS = \frac{10}{N} \log_{10}\left(\sum_i^N x_i^2 \right) \)

Measurement Parameters	Description
Frequency	462.7 MHz
Radio Gain	35 dB
Receiver Sample Rate	2 MHz
Sample Length	N=10,000
Band-pass Filter	6 kHz
Transmitters	0 to 2
Transmission Power	1 W

Receivers consist of Ettus USRP X310 and B210 radios, and a mix of wide- and narrow-band antennas, as shown in the table below Each receiver took measurements with a receiver gain of 35 dB. However, devices have different maxmimum gain settings, and no calibration data was available, so all RSS values in the dataset are uncalibrated, and are only relative to the device.

Usage Instructions

Data is provided in .json format, both as one file and as split files.

import json
data_file = 'powder_462.7_rss_data.json'
with open(data_file) as f:
  data = json.load(f)

The json data is a dictionary with the sample timestamp as a key. Within each sample are the following keys:

• rx_data: A list of data from each receiver. Each entry contains RSS value, latitude, longitude, and device name.
• tx_coords: A list of coordinates for each transmitter. Each entry contains latitude and longitude.
• metadata: A list of dictionaries containing metadata for each transmitter, in the same order as the rows in tx_coords

File Separations and Train/Test Splits

In the separated_data.zip folder there are several train/test separations of the data.

• all_data contains all the data in the main JSON file, separated by the number of transmitters.
• stationary consists of 3 cases where a stationary receiver remained in one location for several minutes. This may be useful for evaluating localization using mobile shuttles, or measuring the variation in the channel characteristics for stationary receivers.
• train_test_splits contains unique data splits used for training and evaluating ML models. These splits only used data from the single-tx case. In other words, the union of each splits, along with unused.json, is equivalent to the file all_data/single_tx.json.
  • The random split is a random 80/20 split of the data.
  • special_test_cases contains the stationary transmitter data, indoor transmitter data (with high noise in GPS location), and transmitters off campus.
  • The grid split divides the campus region in to a 10 by 10 grid. Each grid square is assigned to the training or test set, with 80 squares in the training set and the remainder in the test set. If a square is assigned to the test set, none of its four neighbors are included in the test set. Transmitters occuring in each grid square are assigned to train or test. One such random assignment of grid squares makes up the grid split.
  • The seasonal split contains data separated by the month of collection, in April or July.
  • The transportation split contains data separated by the method of movement for the transmitter: walking, cycling, or driving. The non-driving.json file contains the union of the walking and cycling data.
  • campus.json contains the on-campus data, so is equivalent to the union of each split, not including unused.json.

Digital Surface Model

The dataset includes a digital surface model (DSM) from a State of Utah 2013-2014 LiDAR survey. This map includes the University of Utah campus and surrounding area. The DSM includes buildings and trees, unlike some digital elevation models.

To read the data in python:

import rasterio as rio
import numpy as np
import utm

dsm_object = rio.open('dsm.tif')
dsm_map = dsm_object.read(1)   # a np.array containing elevation values
dsm_resolution = dsm_object.res   # a tuple containing x,y resolution (0.5 meters) 
dsm_transform = dsm_object.transform   # an Affine transform for conversion to UTM-12 coordinates
utm_transform = np.array(dsm_transform).reshape((3,3))[:2]
utm_top_left = utm_transform @ np.array([0,0,1])
utm_bottom_right = utm_transform @ np.array([dsm_object.shape[0], dsm_object.shape[1], 1])
latlon_top_left = utm.to_latlon(utm_top_left[0], utm_top_left[1], 12, 'T')
latlon_bottom_right = utm.to_latlon(utm_bottom_right[0], utm_bottom_right[1], 12, 'T')

Dataset Acknowledgement: This DSM file is acquired by the State of Utah and its partners, and is in the public domain and can be freely distributed with proper credit to the State of Utah and its partners. The State of Utah and its partners makes no warranty, expressed or implied, regarding its suitability for a particular use and shall not be liable under any circumstances for any direct, indirect, special, incidental, or consequential damages with respect to users of this product.

DSM DOI: https://doi.org/10.5069/G9TH8JNQ

China's total Imports in 2024 were valued at US$2.59 Trillion, according to the United Nations COMTRADE database on international trade. China's main import partners were: South Korea, the United States and Japan. The top three import commodities were: Electrical, electronic equipment; Mineral fuels, oils, distillation products and Ores slag and ash. Total Exports were valued at US$3.58 Trillion. In 2024, China had a trade surplus of US$991.41 Billion.

South Korea's total Imports in 2024 were valued at US$631.73 Billion, according to the United Nations COMTRADE database on international trade. South Korea's main import partners were: China, the United States and Japan. The top three import commodities were: Mineral fuels, oils, distillation products; Electrical, electronic equipment and Machinery, nuclear reactors, boilers. Total Exports were valued at US$683.13 Billion. In 2024, South Korea had a trade surplus of US$51.40 Billion.

China's total Exports in 2024 were valued at US$3.58 Trillion, according to the United Nations COMTRADE database on international trade. China's main export partners were: the United States, Hong Kong and Vietnam. The top three export commodities were: Electrical, electronic equipment; Machinery, nuclear reactors, boilers and Vehicles other than railway, tramway. Total Imports were valued at US$2.59 Trillion. In 2024, China had a trade surplus of US$991.41 Billion.

China recorded a trade surplus of 103.22 USD Billion in May of 2025. This dataset provides - China Balance of Trade - actual values, historical data, forecast, chart, statistics, economic calendar and news.

United States Imports from Russia was US$3.27 Billion during 2024, according to the United Nations COMTRADE database on international trade. United States Imports from Russia - data, historical chart and statistics - was last updated on July of 2025.

Australia's total Imports in 2024 were valued at US$296.48 Billion, according to the United Nations COMTRADE database on international trade. Australia's main import partners were: China, the United States and Japan. The top three import commodities were: Vehicles other than railway, tramway; Machinery, nuclear reactors, boilers and Mineral fuels, oils, distillation products. Total Exports were valued at US$340.85 Billion. In 2024, Australia had a trade surplus of US$44.37 Billion.

The United States recorded a capital and financial account deficit of 14200 USD Million in April of 2025. This dataset provides the latest reported value for - United States Net Treasury International Capital Flows - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Russia's total Imports in 2021 were valued at US$293.50 Billion, according to the United Nations COMTRADE database on international trade. Russia's main import partners were: China, Germany and the United States. The top three import commodities were: Machinery, nuclear reactors, boilers; Electrical, electronic equipment and Vehicles other than railway, tramway. Total Exports were valued at US$492.31 Billion. In 2021, Russia had a trade surplus of US$198.82 Billion.

Indonesia's total Exports in 2023 were valued at US$258.77 Billion, according to the United Nations COMTRADE database on international trade. Indonesia's main export partners were: China, the United States and Japan. The top three export commodities were: Mineral fuels, oils, distillation products; Animal, vegetable fats and oils, cleavage products and Iron and steel. Total Imports were valued at US$221.89 Billion. In 2023, Indonesia had a trade surplus of US$36.89 Billion.

Canada's total Exports in 2024 were valued at US$569.17 Billion, according to the United Nations COMTRADE database on international trade. Canada's main export partners were: the United States, China and the United Kingdom. The top three export commodities were: Mineral fuels, oils, distillation products; Vehicles other than railway, tramway and Machinery, nuclear reactors, boilers. Total Imports were valued at US$558.45 Billion. In 2024, Canada had a trade surplus of US$10.72 Billion.

Japan's total Imports in 2024 were valued at US$742.67 Billion, according to the United Nations COMTRADE database on international trade. Japan's main import partners were: China, the United States and Australia. The top three import commodities were: Mineral fuels, oils, distillation products; Electrical, electronic equipment and Machinery, nuclear reactors, boilers. Total Exports were valued at US$707.39 Billion. In 2024, Japan had a trade deficit of US$35.28 Billion.

The European Union's total Exports in 2024 were valued at US$2.80 Trillion, according to the United Nations COMTRADE database on international trade. The European Union's main export partners were: the United States, the United Kingdom and China. The top three export commodities were: Machinery, nuclear reactors, boilers; Pharmaceutical products and Vehicles other than railway, tramway. Total Imports were valued at US$2.64 Trillion. In 2024, The European Union had a trade surplus of US$159.68 Billion.