This dataset provides the raw anonymised (quantitative) data from the EDSA demand analysis. This data has been gathered from surveys performed with those who identify as data scientists and manages of data scientists in different sectors across Europe. The coverage of the data includes level of current expertise of the individual or team (data scientist and manager respectively) in eight key areas. The dataset also includes the importance of the eight key areas as capabilities of a data scientist. Further the dataset includes a breakdown of key tools, technologies and training delivery methods required to enhance the skill set of data scientists across Europe. The EDSA dashboard provides an interactive view of this dataset and demonstrates how it is being used within the project. The dataset forms part of the European Data Science Academy (EDSA) project which received funding from the European Unions's Horizon 2020 research and innovation programme under grant agreement No 643937. This three year project ran/runs from February 2015 to January 2018. Important note on privacy: This dataset has been collected and made available in a pseudo anonymous way, as agreed by participants. This means that while each record represents a person, no sensitive identifiable information, such as name, email or affiliation is available (we don't even collect it). Pseudo anonymisation is never full proof, however the projects privacy impact assessment has concluded that the risk resulting from the de-anonymisation of the data is extremely low. It should be noted that data is not included of participants who did not explicitly agree that it could be shared pseudo anonymously (this was due to a change of terms after the survey had started gathering responses, meaning any early responses had come from people who didn't see this clause). If you have any concerns please contact the data publisher via the links below.

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

In this course, you will explore a variety of open-source technologies for working with geosptial data, performing spatial analysis, and undertaking general data science. The first component of the class focuses on the use of QGIS and associated technologies (GDAL, PROJ, GRASS, SAGA, and Orfeo Toolbox). The second component of the class introduces Python and associated open-source libraries and modules (NumPy, Pandas, Matplotlib, Seaborn, GeoPandas, Rasterio, WhiteboxTools, and Scikit-Learn) used by geospatial scientists and data scientists. We also provide an introduction to Structured Query Language (SQL) for performing table and spatial queries. This course is designed for individuals that have a background in GIS, such as working in the ArcGIS environment, but no prior experience using open-source software and/or coding. You will be asked to work through a series of lecture modules and videos broken into several topic areas, as outlined below. Fourteen assignments and the required data have been provided as hands-on opportunites to work with data and the discussed technologies and methods. If you have any questions or suggestions, feel free to contact us. We hope to continue to update and improve this course. This course was produced by West Virginia View (http://www.wvview.org/) with support from AmericaView (https://americaview.org/). This material is based upon work supported by the U.S. Geological Survey under Grant/Cooperative Agreement No. G18AP00077. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey. After completing this course you will be able to: apply QGIS to visualize, query, and analyze vector and raster spatial data. use available resources to further expand your knowledge of open-source technologies. describe and use a variety of open data formats. code in Python at an intermediate-level. read, summarize, visualize, and analyze data using open Python libraries. create spatial predictive models using Python and associated libraries. use SQL to perform table and spatial queries at an intermediate-level.

A dataset of functional signatures in Great Britain. Functional signatures encompass areas of similar functional usage, derived from grouping together small-scale spatial units, based on similarity in data ranging from remote sensing to land use, census and points of interest data.

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.

LANDISVIEW is a tool, developed at the Knowledge Engineering Laboratory at Texas A&M University, to visualize and animate 8-bit/16-bit ERDAS GIS format (e.g., LANDIS and LANDIS-II output maps). It can also convert 8-bit/16-bit ERDAS GIS format into ASCII and batch files. LANDISVIEW provides two major functions: 1) File Viewer: Files can be viewed sequentially and an output can be generated as a movie file or as an image file. 2) File converter: It will convert the loaded files for compatibility with 3rd party software, such as Fragstats, a widely used spatial analysis tool. Some available features of LANDISVIEW include: 1) Display cell coordinates and values. 2) Apply user-defined color palette to visualize files. 3) Save maps as pictures and animations as video files (*.avi). 4) Convert ERDAS files into ASCII grids for compatibility with Fragstats. (Source: http://kelab.tamu.edu/)

NZEUC 2022 Agenda Esri Technology Spatial Analysis and Data Science - PDF

This presentation explores methodologies and establishes protocols for developing workbenches for spatial data science in research, teaching, and business applications. The objectives of this workbench are to provide:(1) An easy, efficient and customizable toolkit for spatial data analysis with newly added nodes, (2) An integration of data, methodology, and applications for spatial data science, (3) Workflow-based case studies for teaching and research in spatial social science, (4) A training base for users with no skills in GIS and advanced methodology.

Local statistical analysis has long been of interest to social and environmental scientists who analyze geographic data. Research into local spatial statistics experienced a step-change in the mid-1990s, which provided a large class of local statistical methods and models. The local Moran statistic is one commonly used local indicator of spatial association, able to detect both areas of similarity and observations that are very dissimilar from their surroundings. From this, many further local statistics have been developed to characterize spatial clusters and outliers. These statistics have seen limited adoption because they do not sufficiently model the relationships involved in confounded spatial data, where the analyst seeks to understand the local spatial structure of a given outcome variable that is influenced by one or more additional factors. Recent innovations used to do joint multivariate local analysis also do not model this kind of conditional local structure in data. This article provides tools to rigorously characterize confounded local inference and a new and different class of multivariate conditional local Moran statistics that can account for confounding. To do this, we return to the Moran scatterplot as the critical tool for local Moran-style covariance statistics. Extending this concept, a new method is available directly from a “Moran-form” multiple regression. We show the empirical and theoretical properties of this statistic, show how some existing heuristic approaches arise naturally from this framework, and show how the use of conditional inference can change interpretations in an empirical analysis of rent and housing stock in a rapidly changing neighborhood.

Preface:

This is a part of my contribution to the Kiva, to help them continue and expand their initiative to alleviate global poverty.

Context:

In order for Kiva to best set its investment priorities, help inform lenders, and understand their target communities, knowing the level of poverty for each borrower is crucial. However, attaining individual-level information is time-consuming, labor-intensive and expensive.

Therefore, I propose a method that combines machine learning and satellite imagery to predict poverty. This approach is easier, less expensive and scalable since satellite data is often inexpensive and open source. Before developing this model we have to first collect the data that would be relevant for prediction regionalized poverty, which is why I have created this dataset.

Content:

This data set is a collection of the following:

1. Environmental Data: Vegetation indices, soil characteristics, evaporation
2. Climate Data: Temperature, precipitation, elevation
3. Socioeconomic and Demographic Data: Population Density, Access to major cities, Nightlight, Land usage
4. Conflict Data: Conflicts, death tolls, civilian casualties
5. Natural Disaster Data (coming shortly)

I have provided the data in the following formats:

• Individual Level Data: For each region in the loans (kiva_loans) and MPI study set, I have extracted all of the information listed above. The data is provided in the csv files; MPIData_augmented.csv, kivaData_augmented.csv
• Stacked satellite images for each country with a loan. For each country, a satellite image is provided as a .grd file in the Satellite Imagery folder

Usage:

If you are unfamiliar with working with satellite images I suggest you utilize the csv files. I will put out a tutorial on working with the satellite images in the near future. If you have any questions, please post them an I will try to answer them as soon as I can. If you have any questions related to the data, please refer to the data dictionary.

Documentation:

Documentation for this dataset is an ongoing process given the complex and extensive process to preparing this dataset, amd the wide range of data sources. If you have a particular question please post it and I'll answer it as soon as possible.

Upcoming Work:

As I mentioned above, I am going to use this data to build a machine learning model that will be able to predict the poverty for any region in any impoverished country! Stay tuned!

Understanding the characteristics of the rapidly evolving geospatial software ecosystem in the United States is critical to enable convergence research and education that are dependent on geospatial data and software. This paper describes a survey approach to better understand geospatial use cases, software and tools, and limitations encountered while using and developing geospatial software. The survey was broadcast through a variety of geospatial-related academic mailing lists and listservs. We report both quantitative responses and qualitative insights. As 42% of respondents indicated that they viewed their work as limited by inadequacies in geospatial software, ample room for improvement exists. In general, respondents expressed concerns about steep learning curves and insufficient time for mastering geospatial software, and often limited access to high-performance computing resources. If adequate efforts were taken to resolve software limitations, respondents believed they would be able to better handle big data, cover broader study areas, integrate more types of data, and pursue new research. Insights gained from this survey play an important role in supporting the conceptualization of a national geospatial software institute in the United States with the aim to drastically advance the geospatial software ecosystem to enable broad and significant research and education advances.

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.

A .csv file containing the simulated longitude (column 1) and latitude (column 2) coordinates for 8 zones across North Florida

The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2023-5 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The Science data are the initial annual model outputs that consist of two images: percent tree canopy cover (TCC) and standard error. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset, and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the years 1985 through 2023 are available. The Science data were produced using a random forest regression algorithm. For standard error data, the initial standard error estimates that ranged from 0 to approximately 45 were multiplied by 100 to maintain data precision (e.g., 45 = 4500). Therefore, standard error estimates pixel values range from 0 to approximately 4500. The value 65534 represents the non-processing area mask where no cloud or cloud shadow-free data are available to produce an output, and 65535 represents the background value. The Science data are accessible for multiple user communities, through multiple channels and platforms. For information on the NLCD TCC data and processing steps see the NLCD metadata. Information on the Science data and processing steps are included here. Data Download and Methods Documents: - https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/ This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

The Center for Spatial Information Science and Systems (CSISS) is an interdisciplinary research center chartered by the provost and affiliated with the College of Science at George Mason University, Fairfax VA, 22030, U.S.A.CSISS currently operates Laboratory for Advanced Information Technology and Standards (LAITS)CSISS is a member of the National Committee on Information Technology Standards Technical Committee L1 and a member of Open GIS Consortium (OGC).CSISS Misson:* To conduct world-class research in spatial information science and system.* To provide state-of-art research training to post-doctoral fellows, Ph.D. and Master students in the field.CSISS Research Focus:* Theory and methodology of spatial information science;* Standards and Interoperability of spatial data, information, knowledge, and systems;* Architecture and prototype of widely distributed large spatial information systems, such as NSDI, GSDI, and GEOSS, as well as service-based spatial knowledge and decision-making systems;* Exploration of new information technologies that have potential applications in Spatial Information Science (SIS);* The applications of SIS in the social sectors having either national interests or major commercial values, such as renewable energy, location-based mobile services, intelligent transportation, and homeland security.

In summer 2021, the Bureau of Land Management - Nevada (BLM NV) requested that the U.S. Geological Survey Mineral Resources Program (USGS MRP), acting through the Geology, Minerals, Energy, and Geophysics Science Center (GMEGSC) and other collaborating USGS Science Centers, conduct a scoping study to: 1) discover mineral resource data and relevant publications that have been released since the publication of the 2016 USGS Sagebrush Focal Area Mineral Resource Assessment (SaMiRA; Day and others, 2016) for the Nevada portion of the Focal Area, and 2) evaluate whether these data support an update to the assessment for the SFA lands in Nevada. The commodities of interest for this scoping study are: copper, gold, lead, lithium, molybdenum, silver, tungsten, vanadium, zinc, and rare earth elements (REEs). This report is being released as a restricted file federal interagency report (RFFIR). This data release comprises the geospatial files used in the post-2016 assessment. It includes a ...

If you have ever found yourself using the word “where” when describing what you are doing, or the problem you are trying to solve, then spatial data science should have been part of the solution. Understanding location and how it affects the relationship between things is crucial when trying to solve many real-world problems.

The High Plains aquifer extends from approximately 32 to 44 degrees north latitude and 96 degrees 30 minutes to 106 degrees west longitude. The aquifer underlies about 175,000 square miles in parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming. This dataset consists of a raster of estimated water-level changes for the High Plains aquifer from pre-irritation season, 2015 to pre-irritation season 2017. This digital dataset was created using water-level measurements from 7,699 wells measured in both 2015 and 2017. The map was reviewed for consistency with the relevant data at a scale of 1:1,000,000.

The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2021-4 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The NLCD product suite includes data for years 2011 through 2021. The NCLD data are processed to remove small interannual changes from the annual TCC timeseries, and to mask TCC pixels that are known to be 0 percent TCC, non-tree agriculture, and water. A small interannual change is defined as a TCC change less than an increase or decrease of 10 percent compared to a TCC baseline value. The initial TCC baseline value is the median of 2008-2010 TCC data. For each year following 2011, on a pixel-wise basis TCC values are updated to a new baseline value if an increase or decrease of 10 percent TCC occurs relative to the 2008-2010 TCC baseline value. If no increase or decrease greater than 10 percent TCC occurs relative to the 2008-2010 baseline, then the 2008-2010 TCC baseline value is caried through to the next year in the timeseries. Pixel values range from 0 to 100 percent. The non-processing area is represented by value 254, and the background is represented by the value 255. The Science and NLCD tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms. For information on the Science data and processing steps see the Science metadata. Information on the NLCD data and processing steps are included here. Data Download and Methods Documents: - https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/ This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

Introduction and Rationale:Due to our increasing understanding of the role the surrounding landscape plays in ecological processes, a detailed characterization of land cover, including both agricultural and natural habitats, is ever more important for both researchers and conservation practitioners. Unfortunately, in the United States, different types of land cover data are split across thematic datasets that emphasize agricultural or natural vegetation, but not both. To address this data gap and reduce duplicative efforts in geospatial processing, we merged two major datasets, the LANDFIRE National Vegetation Classification (NVC) and USDA-NASS Cropland Data Layer (CDL), to produce integrated ‘Spatial Products for Agriculture and Nature’ (SPAN). Our workflow leveraged strengths of the NVC and the CDL to produce detailed rasters comprising both agricultural and natural land-cover classes. We generated SPAN for each year from 2012-2021 for the conterminous United States, quantified agreement between input layers and accuracy of our merged product, and published the complete workflow necessary to update SPAN. In our validation analyses, we found that approximately 5.5% of NVC agricultural pixels conflicted with the CDL, but we resolved a majority of these conflicts based on surrounding agricultural land, leaving only 0.6% of agricultural pixels unresolved in the final version of SPAN.Contents:Spatial dataNational rasters of land cover in the conterminous United States: 2012-2021Rasters of pixels mismatched between CDL and NVC: 2012-2021Resources in this dataset:Resource Title: SPAN land cover in the conterminous United States: 2012-2021 - SCINet File Name: KammererNationalRasters.zip Resource Description: GeoTIFF rasters showing location of pixels that are mismatched between 2016 NVC and specific year of CDL (2012-2021). Spatial Products for Agriculture and Nature ('SPAN') land cover in the conterminous United States from 2012-2021. This raster dataset is available in GeoTIFF format and was created by joining agricultural classes from the USDA-NASS Cropland Data Layer (CDL) to national vegetation from the LANDFIRE National Vegetation Classification v2.0 ('Remap'). Pixels of national vegetation are the same in all rasters provided here and represent land cover in 2016. Agricultural pixels were taken from the CDL in the specified year, so depict agricultural land from 2012-2021. Resource Title: Rasters of pixels mismatched between CDL and NVC: 2012-2021 - SCINet File Name: MismatchedNational.zip Resource Description: GeoTIFF rasters showing location of pixels that are mismatched between 2016 NVC and specific year of CDL (2012-2021). This dataset includes pixels that were classified as agriculture in the NVC but, in the CDL, were not agriculture (or were a conflicting agricultural class). For more details, we refer users to the linked publication describing our geospatial processing and validation workflow.SCINet users: The files can be accessed/retrieved with valid SCINet account at this location: /LTS/ADCdatastorage/NAL/published/node455886/ See the SCINet File Transfer guide for more information on moving large files: https://scinet.usda.gov/guides/data/datatransferGlobus users: The files can also be accessed through Globus by following this data link. The user will need to log in to Globus in order to retrieve this data. User accounts are free of charge with several options for signing on. Instructions for creating an account are on the login page.