Co-sponsored by the Center for Geographic Analysis of Harvard University, RMDS Lab and Future Data Lab, the workflow-based data analysis project aims to provide new approach for efficient data analysis and replicable, reproducible and expandable research. This year-round webinar series is designed to help attendees advance in their career with research data, tools, and their applications.

This webinar introduced the project of "the workbench for spatial data science" sponsored by the spatial data lab.

This seminar will introduce the KNIME Analytics Platform and its Geospatial Analytics extension developed by the Spatial Data Lab (SDL) team at Harvard's Center for Geographic Analysis (CGA). The SDL team members will share the presentations, presenting the project's vision and demonstrating the new way of performing geospatial analysis in a codeless visual way with case studies.

This webinar introduced the implementations of effective data management and spatial analytics by academic and industry leaders of the field, and discuss the directions for further enhancement through integration with a cloud-based platform for research data sharing and workflow-based data analytics.

Updated to May 13, 2021. World COVID-19 daily cases with basemap, starting from January 22, 2020.

As a joint effort by scholars and professionals from the Center for Geographical Analysis at Harvard University, the Geo-Computation Center for Social Sciences at Wuhan University, the China Data Institute, the NSF Spatiotemporal Innovation Center, RMDS Lab, and some other institutions, an initiative on “Resources for COVID-19 Study” was sponsored by the China Data Lab project (http://chinadatalab.net). The objectives of this project are: (1) to provide data support for the spatial study of COVID-19 at local, regional and global levels with information collected and integrated from different sources; (2) to facilitate quantitative research on spatial spreading and impacts of COVID-19 with advanced methodology and technology; (3) to promote collaborative research on the spatial study of COVID-19 on the Spatial Data Lab and Dataverse platforms; and (4) to build research capacity for future collaborative projects. The project has sponsored two webinar series on Covid-19 data and modeling (see links to recorded webinars below). This is the 3rd webinar series with a focus on the impact analysis of COVID-19 pandemic.

Instructions for installing workflow tools and running workflows

This case study shows how to use workflow KNIME to analyze COVID-19 related tweets based on text mining methods.

CDL webinars.

This webinar series is for data introductions.. Visit https://dataone.org/datasets/sha256%3Ac3cc2b88b9990c87242567425e238e1c65447f91807c2cc814230250e07920af for complete metadata about this dataset.

Health facilities POI, such as the hospital.. Visit https://dataone.org/datasets/sha256%3A65a43f42cdde88a56bb7c279a23979ecb107a8429f963556fb7807bbecab0635 for complete metadata about this dataset.

Workflows for accessing and analyzing COVID-19 mobility data.

Using a variety of inputs, IFPRI's Spatial Production Allocation Model (SPAM) uses a cross-entropy approach to make plausible estimates of crop distribution within disaggregated units. Moving the data from coarser units such as countries and sub-national provinces, to finer units such as grid cells, reveals spatial patterns of crop performance, creating a global grid-scape at the confluence between geography and agricultural production systems. Improving spatial understanding of crop production systems allows policymakers and donors to better target agricultural and rural development policies and investments, increasing food security and growth with minimal environmental impacts.

Updated to Mar 23, 2023. It contains COVID-19 Daily Cases with US basemap, including state, county-level, and metropolitan data.

The AIRMISR_HARVARD_2003 data set was acquired during a flight over the Harvard Forest, Massachusetts, USA, target as part of the AirMISR deployments from the Wallops Flight Facility during the August 2003 campaign. This particular flight took place on August 24, 2003. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. There were a total of two runs during this flight. A run comprises data collected from nine view angles acquired on a fixed flight azimuth angle. Each data file from one run contains either: a) Level 1B1 Radiometric product from one of the 9 camera angles or b) Level 1B2 Georectified radiance product from one of the 9 camera angles. Browse images in PNG format are available for the Level 1B1 product and browse images in JPEG format are available for the Level 1B2 product. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and for ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared. Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2). The Level 1 Radiometric product contains data that are scaled to convert the digital output of the cameras to radiances and are conditioned to remove instrument-dependent effects. Additionally, all radiances are adjusted to remove slight spectral sensitivity differences among the detector elements of each spectral band. These data have a 7-meter spatial resolution at nadir and around 30-meter at the most oblique 70.5 degree angles. The Level 1 Georectified radiance product contains the Level 1 radiometric product resampled to a 27.5 meter spatial resolution and mapped into a standard Universal Transverse Mercator (UTM) map projection. Initially the data are registered to each camera angle and to the ground. This processing is necessary because the nine views of each point on the ground are not acquired simultaneously. Once the map grid center points are located in the AirMISR imagery through the process of georectification, a radiance value obtained from the surrounding AirMISR pixels is assigned to that map grid center. Bilinear interpolation is used as the basis for computing the new radiance. A UTM grid point falling somewhere in the image data will have up to 4 surrounding points. The bilinear interpolated value is obtained using the fractional distance of the interpolation point in the cross-track direction and the fractional distance in the along-track direction.

This webinar series introduce some research data with a focus on China and discuss the difference from the US data. Each webinar will cover the following topics: (1) data sources, data collection, data category, definition, description, and interpretation; (2) alternative data and derivable data from other data sources, especially some big data sources; (3) comparison of data difference between the US and China; (4) available tools for efficient data analysis; (5) discussions on pros and cons; and (6) data applications in research and teaching.

This dataset stores workflows related to literature analysis.

Updated to Apr 17, 2023. Metropolitan level daily cases. There are 926 metropolitans except for the areas in Perto Rico.

Updated to August 30, 2020. Policies and regulations released by the Chinese government, global organizations, western countries, and so on. It is categorized as Chinese News Timeline, which is updated to August 30, 2020, and Global News Timeline, which is updated to August 21, 2020.

The dataset is updated to 2022-05-13. The original data is from Google Comunity Mobility Reports. The reports are integrated with US state and county basemap and the dataset is updated since February 15, 2020.