Resources by jona.frroku

COVID-19 County-Level Data with Political Classification (U.S., 2020)

COVID-19 County-Level Data with Political Classification (U.S., 2020)

jona.frroku
Open Dataset
Creators: The New York Times (COVID data); Harvard Dataverse; National Governors Association
Publication Date: 2023/07
Creators: The New York Times (COVID data); Harvard Dataverse; National Governors Association

This dataset underpins the analysis conducted by Eutsler, Harris, Williams, and Cornejo (2023) in Accounting, Organizations and Society, which investigates partisan influences on the reporting of COVID-19 cases and deaths in the United States. The authors apply Benford’s Law to examine irregularities in county-level COVID-19 statistics from January 21 to November 3, 2020. Their study integrates COVID-19 case data from The New York Times, political affiliation data from the 2020 gubernatorial roster (National Governors Association), and county-level political leanings from the 2016 U.S. presidential election. The result is a composite dataset linking epidemiological reporting behavior with political partisanship indicators to assess statistical conformity and potential misreporting.

Firm-to-Firm Matching and Agglomeration Data (Japan)

Firm-to-Firm Matching and Agglomeration Data (Japan)

jona.frroku
Open Dataset
Creators: Yuhei Miyauchi
Publication Date: 2024/09/15
Creators: Yuhei Miyauchi

This dataset supports the Econometrica article by Yuhei Miyauchi (2024), which investigates the role of matching frictions and market thickness in shaping spatial agglomeration through firm-to-firm trade in Japan. Using panel data that tracks supplier-client relationships before and after supplier bankruptcies, the study quantifies the impact of geographic supplier density on rematching speed. The dataset includes firm-level trade and location data, simulation outputs from the general equilibrium model, and empirical estimates that validate the thick market externality. The materials enable replication of the reduced-form analyses and the calibration of the spatial equilibrium model that links population density to regional wages.

Chinese Social Media and Protest Dynamics Dataset (2009–2017)

Chinese Social Media and Protest Dynamics Dataset (2009–2017)

jona.frroku
Open Dataset
Creators: Sina Weibo
Publication Date: 2024/08/14
Creators: Sina Weibo

This dataset accompanies the Econometrica article by Bei Qin, David Strömberg, and Yanhui Wu (2024), which analyzes the role of social media in shaping protest and strike dynamics across China from 2009 to 2017. It includes data on 13.2 billion Weibo (microblog) posts—tweets and retweets—used to measure social media communication across Chinese cities. The study exploits the staggered rollout of Weibo access to identify the causal impact of digital communication on the geographical spread, diversification, and escalation of protests. The dataset provides the foundation for modeling protest wave propagation and network-based diffusion of social action in tightly monitored environments.

COVID-19 infection cases and deaths in the United States

COVID-19 infection cases and deaths in the United States

jona.frroku
Open Dataset
Creators: The New York Times
Publication Date: 2020/01/21 - 2020/11/03
Creators: The New York Times

This dataset, compiled and maintained by The New York Times, contains daily counts of COVID-19 cases and deaths at the county level in the United States. It was used in the study “Accounting for partisanship and politicization: Employing Benford’s Law to examine misreporting of COVID-19 infection cases and deaths in the United States” to assess potential misreporting of COVID-19 statistics. The dataset spans from early 2020 onwards and includes columns such as date, county, state, FIPS code, cumulative cases, and cumulative deaths.

USGS Earth Explorer

USGS Earth Explorer

jona.frroku
Open Dataset
Creators: U.S. Geological Survey (USGS)
Publication Date: 1972
Creators: U.S. Geological Survey (USGS)

USGS Earth Explorer is a powerful search platform that provides free access to satellite imagery, aerial photography, and remote sensing data. With its extensive archive, the Earth Explorer offers data from various satellites like Landsat, Sentinel, and ASTER. The platform’s extensive archive encompasses data from multiple satellite missions, with each mission contributing a significant volume of data. For instance, Landsat 9 alone collects up to 750 scenes per day, and together with Landsat 8, they add nearly 1,500 new scenes daily to the USGS archive. Given that each Landsat scene is approximately 1 GB in size, the daily data addition from these two satellites alone can be estimated at around 1.5 TB. The number of observations within Earth Explorer is continually increasing as satellites like Landsat 8 and 9 are operational and consistently capturing data. Landsat 9, for example, collects as many as 750 scenes per day, and together with Landsat 8, they add nearly 1,500 new scenes daily to the USGS archive. The temporal coverage of datasets accessible via Earth Explorer varies depending on the specific mission or dataset. For instance, the Landsat program offers a historical archive dating back to 1972, providing over five decades of Earth observation data. Structurally, Earth Explorer organizes its datasets based on the source and type of data. For example, Landsat data is categorized by satellite and processing level, including Level-1 and Level-2 products, with varying spatial resolutions. Similarly, other datasets are organized according to their respective missions and data characteristics, allowing users to efficiently locate and utilize the data relevant to their specific needs.

ESA’s Sentinel Data

ESA’s Sentinel Data

jona.frroku
Open Dataset
Creators: European Space Agency (ESA)
Publication Date: 2014
Creators: European Space Agency (ESA)

The Sentinel satellite constellation provides Earth observation data under the Copernicus program, offering free and open access to a wide range of environmental information. The data covers various spectral, spatial, and temporal scales, enabling applications in environmental monitoring, land-use change, climate research, disaster management, and ocean observation. Sentinel-1 is equipped with C-band Synthetic Aperture Radar (SAR) that provides all-weather, day-and-night radar imagery, making it particularly useful for monitoring land and ocean surfaces, including applications like mapping, forestry, soil moisture estimation, and sea-ice observations. Sentinel-2 features a multispectral optical sensor capturing 13 spectral bands, offering high-resolution imagery (up to 10 meters), which is ideal for land cover classification, agricultural monitoring, and disaster management. Sentinel-3 carries multiple instruments to measure sea-surface topography, sea and land surface temperature, and ocean and land color, supporting ocean forecasting, environmental and climate monitoring, and land-use change detection. The data volume varies depending on the specific mission and product type. Sentinel-1 data files range from 1 GB to 6 GB per scene, depending on the observation mode and product type. Sentinel-2 Level-1C products, which cover a 100×100 km area, range from approximately 500 MB to 1 GB. Sentinel-3 product sizes vary widely depending on the instrument and processing level, ranging from several megabytes to over a gigabyte per product. The number of observations is continually increasing as the satellites remain operational and continuously capture data. Sentinel-1 revisits each region every 6 to 12 days, depending on the observation mode. Sentinel-2 provides global coverage of land surfaces every 5 days with its two satellites, Sentinel-2A and Sentinel-2B. Sentinel-3 offers a near-daily revisit for optical instruments, with shorter revisit times for altimetry measurements. Sentinel-1A was launched in April 2014, followed by Sentinel-1B in April 2016, and Sentinel-1C in December 2024. Sentinel-2A was launched in June 2015, Sentinel-2B in March 2017, and Sentinel-2C in September 2024. Sentinel-3A was launched in February 2016, and Sentinel-3B followed in April 2018.

NOAA (National Oceanic and Atmospheric Administration) Class

NOAA (National Oceanic and Atmospheric Administration) Class

jona.frroku
Open Dataset
Creators: National Oceanic and Atmospheric Administration (NOAA)
Publication Date: 1970
Creators: National Oceanic and Atmospheric Administration (NOAA)

NOAA’s CLASS (Comprehensive Large Array-data Stewardship System) is a web-based data repository that archives and distributes environmental data collected by various NOAA satellites and missions. CLASS provides access to a variety of data types, including:Environmental satellite data, weather data, other datasets. CLASS archives data from various NOAA satellite missions and instruments, including the Advanced Very High Resolution Radiometer (AVHRR), which captures global cloud cover, sea surface temperatures, and vegetation indices; the Geostationary Operational Environmental Satellites (GOES), which provide real-time weather monitoring and forecasting data; and the Joint Polar Satellite System (JPSS), which offers global environmental data for weather forecasting and climate monitoring. A key feature of CLASS is its role in long-term data preservation, ensuring that environmental information remains accessible for climate research and historical analyses. The system also provides user-friendly access, offering search and retrieval capabilities that allow users to locate and download specific datasets efficiently. The temporal coverage of datasets within CLASS varies by instrument and mission. AVHRR data is available from the late 1970s to the present, GOES data coverage starts from the 1970s and continues to current operations, and JPSS data has been available since 2011.

Sentinel Hub

Sentinel Hub

jona.frroku
Open Dataset
Creators: Sinergise
Publication Date: 1970
Creators: Sinergise

Sentinel Hub provides access to satellite imagery from various missions, including Sentinel-1, Sentinel-2, and Landsat satellites. It offers tools for visualizing, analyzing, and processing satellite data. Sentinel Hub provides access to a wide array of satellite imagery, including data from the Copernicus Sentinel missions (e.g., Sentinel-1, Sentinel-2), Landsat series, and commercial providers like PlanetScope. This extensive dataset supports diverse applications such as agriculture, forestry, land monitoring, and emergency response. The platform offers on-the-fly processing capabilities, allowing users to perform complex analyses without the need for local computational resources. This feature streamlines workflows and accelerates data processing tasks. Users can create custom processing scripts using Sentinel Hub’s scripting language, enabling tailored data analyses and visualization outputs to meet specific project requirements. Sentinel Hub provides access to historical satellite imagery dating back to the launch dates of respective missions. For instance, Sentinel-2 data is available from 2015 onwards, enabling users to analyze changes over the past decade.

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