The establishment of a BES Multi-User Geodatabase (BES-MUG) allows for the storage, management, and distribution of geospatial data associated with the Baltimore Ecosystem Study. At present, BES data is distributed over the internet via the BES website. While having geospatial data available for download is a vast improvement over having the data housed at individual research institutions, it still suffers from some limitations. BES-MUG overcomes these limitations; improving the quality of the geospatial data available to BES researches, thereby leading to more informed decision-making. BES-MUG builds on Environmental Systems Research Institute's (ESRI) ArcGIS and ArcSDE technology. ESRI was selected because its geospatial software offers robust capabilities. ArcGIS is implemented agency-wide within the USDA and is the predominant geospatial software package used by collaborating institutions. Commercially available enterprise database packages (DB2, Oracle, SQL) provide an efficient means to store, manage, and share large datasets. However, standard database capabilities are limited with respect to geographic datasets because they lack the ability to deal with complex spatial relationships. By using ESRI's ArcSDE (Spatial Database Engine) in conjunction with database software, geospatial data can be handled much more effectively through the implementation of the Geodatabase model. Through ArcSDE and the Geodatabase model the database's capabilities are expanded, allowing for multiuser editing, intelligent feature types, and the establishment of rules and relationships. ArcSDE also allows users to connect to the database using ArcGIS software without being burdened by the intricacies of the database itself. For an example of how BES-MUG will help improve the quality and timeless of BES geospatial data consider a census block group layer that is in need of updating. Rather than the researcher downloading the dataset, editing it, and resubmitting to through ORS, access rules will allow the authorized user to edit the dataset over the network. Established rules will ensure that the attribute and topological integrity is maintained, so that key fields are not left blank and that the block group boundaries stay within tract boundaries. Metadata will automatically be updated showing who edited the dataset and when they did in the event any questions arise. Currently, a functioning prototype Multi-User Database has been developed for BES at the University of Vermont Spatial Analysis Lab, using Arc SDE and IBM's DB2 Enterprise Database as a back end architecture. This database, which is currently only accessible to those on the UVM campus network, will shortly be migrated to a Linux server where it will be accessible for database connections over the Internet. Passwords can then be handed out to all interested researchers on the project, who will be able to make a database connection through the Geographic Information Systems software interface on their desktop computer. This database will include a very large number of thematic layers. Those layers are currently divided into biophysical, socio-economic and imagery categories. Biophysical includes data on topography, soils, forest cover, habitat areas, hydrology and toxics. Socio-economics includes political and administrative boundaries, transportation and infrastructure networks, property data, census data, household survey data, parks, protected areas, land use/land cover, zoning, public health and historic land use change. Imagery includes a variety of aerial and satellite imagery. See the readme: http://96.56.36.108/geodatabase_SAL/readme.txt See the file listing: http://96.56.36.108/geodatabase_SAL/diroutput.txt

Analysis of ‘2018 CT Data Catalog (Non GIS)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/fe457197-5afe-4a20-a131-1bdcf9bd8ace on 26 January 2022.

--- Dataset description provided by original source is as follows ---

Catalog of high value data inventories produced by Connecticut executive branch agencies and compiled by the Office of Policy and Management. This catalog does not contain information about high value GIS data, which is compiled in a separate data inventory at the following link: https://data.ct.gov/Government/CT-Data-Catalog-GIS-/p7we-na27

As required by Public Act 18-175, executive branch agencies must annually conduct a high value data inventory to capture information about the high value data that they collect.

High value data is defined as any data that the department head determines (A) is critical to the operation of an executive branch agency; (B) can increase executive branch agency accountability and responsiveness; (C) can improve public knowledge of the executive branch agency and its operations; (D) can further the core mission of the executive branch agency; (E) can create economic opportunity; (F) is frequently requested by the public; (G) responds to a need and demand as identified by the agency through public consultation; or (H) is used to satisfy any legislative or other reporting requirements.

This dataset was last updated 3/4/2019 and will continue to be updated as high value data inventories are submitted to OPM.

--- Original source retains full ownership of the source dataset ---

The research focus in the field of remotely sensed imagery has shifted from collection and warehousing of data ' tasks for which a mature technology already exists, to auto-extraction of information and knowledge discovery from this valuable resource ' tasks for which technology is still under active development. In particular, intelligent algorithms for analysis of very large rasters, either high resolutions images or medium resolution global datasets, that are becoming more and more prevalent, are lacking. We propose to develop the Geospatial Pattern Analysis Toolbox (GeoPAT) a computationally efficient, scalable, and robust suite of algorithms that supports GIS processes such as segmentation, unsupervised/supervised classification of segments, query and retrieval, and change detection in giga-pixel and larger rasters. At the core of the technology that underpins GeoPAT is the novel concept of pattern-based image analysis. Unlike pixel-based or object-based (OBIA) image analysis, GeoPAT partitions an image into overlapping square scenes containing 1,000'100,000 pixels and performs further processing on those scenes using pattern signature and pattern similarity ' concepts first developed in the field of Content-Based Image Retrieval. This fusion of methods from two different areas of research results in orders of magnitude performance boost in application to very large images without sacrificing quality of the output.

GeoPAT v.1.0 already exists as the GRASS GIS add-on that has been developed and tested on medium resolution continental-scale datasets including the National Land Cover Dataset and the National Elevation Dataset. Proposed project will develop GeoPAT v.2.0 ' much improved and extended version of the present software. We estimate an overall entry TRL for GeoPAT v.1.0 to be 3-4 and the planned exit TRL for GeoPAT v.2.0 to be 5-6. Moreover, several new important functionalities will be added. Proposed improvements includes conversion of GeoPAT from being the GRASS add-on to stand-alone software capable of being integrated with other systems, full implementation of web-based interface, writing new modules to extent it applicability to high resolution images/rasters and medium resolution climate data, extension to spatio-temporal domain, enabling hierarchical search and segmentation, development of improved pattern signature and their similarity measures, parallelization of the code, implementation of divide and conquer strategy to speed up selected modules.

The proposed technology will contribute to a wide range of Earth Science investigations and missions through enabling extraction of information from diverse types of very large datasets. Analyzing the entire dataset without the need of sub-dividing it due to software limitations offers important advantage of uniformity and consistency. We propose to demonstrate the utilization of GeoPAT technology on two specific applications. The first application is a web-based, real time, visual search engine for local physiography utilizing query-by-example on the entire, global-extent SRTM 90 m resolution dataset. User selects region where process of interest is known to occur and the search engine identifies other areas around the world with similar physiographic character and thus potential for similar process. The second application is monitoring urban areas in their entirety at the high resolution including mapping of impervious surface and identifying settlements for improved disaggregation of census data.

Advancing Research on Nutrition and Agriculture (AReNA) is a 6-year, multi-country project in South Asia and sub-Saharan Africa funded by the Bill and Melinda Gates Foundation, being implemented from 2015 through 2020. The objective of AReNA is to close important knowledge gaps on the links between nutrition and agriculture, with a particular focus on conducting policy-relevant research at scale and crowding in more research on this issue by creating data sets and analytical tools that can benefit the broader research community. Much of the research on agriculture and nutrition is hindered by a lack of data, and many of the datasets that do contain both agriculture and nutrition information are often small in size and geographic scope. AReNA team constructed a large multi-level, multi-country dataset combining nutrition and nutrition-relevant information at the individual and household level from the Demographic and Health Surveys (DHS) with a wide variety of geo-referenced data on agricultural production, agroecology, climate, demography, and infrastructure (GIS data). This dataset includes 60 countries, 184 DHS, and 122,473 clusters. Over one thousand geospatial variables are linked with DHS. The entire dataset is organized into 13 individual files: DHS_distance, DHS_livestock, DHS_main, DHS_malaria, DHS NDVI, DHS_nightlight, DHS_pasture and climate (mean), DHS_rainfall, DHS_soil, DHS_SPAM, DHS_suit, DHS_temperature, and DHS_traveltime.

Access to those reports within the Historic England Research Reports series that relate to geographically defined sites such as a building where a building assessment or dendrochronology has been carried out, or a field covered by a geophysical survey. It important to note that there a lot of reports in the database that do not have a spatial location e.g. those that are thematic. These are still available via the Research Reports database.

Spatial data relating to reports within the Historic England Research Reports series. This is point data for the grid reference given in the report, or where this was not available from other sources. The majority of reports relate to discrete features such as a building where a building assessment or dendrochronology has been carried out, or a field covered by a geophysical survey. However, there are also a small number of thematic reports that have contained detailed gazetteers allowing multiple links to the same report e.g. Police Stations, Jewish Cemeteries and Shropshire Inns. The most extreme example of this is the Gas Industry report where over 1500 points are used. There are also a number of large area projects covering extensive regions. Because this is a point layer, these are merely covered by a centre point for the feature.

Data updated frequently.

Dissertation and dataset present an archaeological study of the Huarmey Valley region, located on the northern coast of Peru. My work uses modern and innovative digital methods. My research focuses on better understanding the location of one of the most important sites in the valley, Castillo de Huarmey, by learning about the context in which it functioned. The Imperial Mausoleum located at the site, along with the burial chamber beneath it, is considered one of the most important discoveries regarding the Wari culture in recent years.In the dissertation, I address issues concerning both the location of the site on a macro scale - in the entire Huarmey Valley, on a micro scale - the context of the Huarmey Valley delta – and the spatial relationships within the burial chamber located beneath the Mausoleum. I ask the questions (i) How did Castillo de Huarmey communicate with other sites dated to the same period located in the valley and also in adjacent valleys? Did this influence its role in the region? (ii) Is the Mausoleum at Castillo de Huarmey located intentionally and what was the meaning of this location at the micro and macro scale? (iii) What spatial relations existed between Castillo de Huarmey and other sites from the same period? (iv) Does the position of the artifacts, found in situ in the burial chamber, show important relationships between buried individuals? (v) Does spatial analysis show interesting spatial patterns within the burial inside the chamber?The questions can be answered by describing and testing the digital methods proposed in the doctoral dissertation related to both field data collection and their analysis and interpretation. These methods were selected and adapted to a specific area (the Northern Coast of Peru) and to the objective of answering the questions posed in the thesis. The wide range of digital methods used in archaeology is made possible by the use of Geographic Information Systems (abbreviated GIS) in research. To date, GIS in archaeology is used in three aspects (Wheatley and Gillings 2002): (i) statistical and spatial analysis to obtain new information, (ii) landscape archaeology, and (iii) Cultural Resource Management.My dissertation is divided into three main components that discuss the types of digital methods used in archaeology. The division of these methods will be adapted to the level of detail of the research (from the location of the site in the region, to the delta of the Huarmey Valley, to the burial chamber of the Mausoleum) and to the way they are used in archaeology (from Cultural Resource Management, to archaeological landscape analysis, to statistical-spatial analysis). One of the aims of the dissertation is to show the methodological path of the use of digital methods, i.e. from the acquisition of data in the field, through analysis, to their interpretation in a cultural context. However, the main objective of my research is to interpret the spatial relationships from the macro to the micro level, in the case described, against the background of other sites located in the valley, the location of Castillo de Huarmey in the context of the valley delta, and finally to the burial chamber of the Mausoleum. The uniqueness of the described burial makes the research and its results pioneering in nature.As a final result of my work I would like to determine whether relationships can be demonstrated between the women buried in the burial chamber and whether the location of particular categories of artifacts can illustrate specific spatial patterns of burial. Furthermore, my goal is to attempt to understand the relationship between the Imperial Mausoleum and other sites (archival as well as newly discovered) located in the Huarmey Valley and to understand the role of the site's location.Published dataset represents, described in the dissertation, mobile GIS survey on the site PV35-5 created in Survey123, ESRI application; xml and xls used for creating the survey that was used during the research of the site, as well as the results of the survey published in ArcGIS Pro package. The package includes collected data as points, saved as .shp, as well as ortophotomaps (as geotiff) and Digital Elevation Model and hillshade of PV35-5. The published dataset represents part of the dissertation describing archaeological landscape analysis of Huarmey Valley’s delta.

Abstract : The search for the most appropriate GIS data model to integrate, manipulate and analyse spatio-temporal data raises several research questions about the conceptualisation of geographic spaces. Although there is now a general consensus that many environmental phenomena require field and object conceptualisations to provide a comprehensive GIS representation, there is still a need for better integration of these dual representations of space within a formal spatio-temporal database. The research presented in this paper introduces a hybrid and formal dual data model for the representation of spatio-temporal data. The whole approach has been fully implemented in PostgreSQL and its spatial extension PostGIS, where the SQL language is extended by a series of data type constructions and manipulation functions to support hybrid queries. The potential of the approach is illustrated by an application to underwater geomorphological dynamics oriented towards the monitoring of the evolution of seabed changes. A series of performance and scalability experiments are also reported to demonstrate the computational performance of the model.Data Description : The data set used in our research is a set of bathymetric surveys recorded over three years from 2009 to 2011 as Digital Terrain Models (DTM) with 2m grid spacing. The first survey was carried out in February 2009 by the French hydrographic office, the second one was recorded on August-September 2010 and the third in July 2011, both by the “Institut Universitaire Européen de la Mer”.

The USDA Long-Term Agroecosystem Research was established to develop national strategies for sustainable intensification of agricultural production. As part of the Agricultural Research Service, the LTAR Network incorporates numerous geographies consisting of experimental areas and locations where data are being gathered. Starting in early 2019, two working groups of the LTAR Network (Remote Sensing and GIS, and Data Management) set a major goal to jointly develop a geodatabase of LTAR Standard GIS Data Layers. The purpose of the geodatabase was to enhance the Network's ability to utilize coordinated, harmonized datasets and reduce redundancy and potential errors associated with multiple copies of similar datasets. Project organizers met at least twice with each of the 18 LTAR sites from September 2019 through December 2020, compiling and editing a set of detailed geospatial data layers comprising a geodatabase, describing essential data collection areas within the LTAR Network. The LTAR Standard GIS Data Layers geodatabase consists of geospatial data that represent locations and areas associated with the LTAR Network as of late 2020, including LTAR site locations, addresses, experimental plots, fields and watersheds, eddy flux towers, and phenocams. There are six data layers in the geodatabase available to the public. This geodatabase was created in 2019-2020 by the LTAR network as a national collaborative effort among working groups and LTAR sites. The creation of the geodatabase began with initial requests to LTAR site leads and data managers for geospatial data, followed by meetings with each LTAR site to review the initial draft. Edits were documented, and the final draft was again reviewed and certified by LTAR site leads or their delegates. Revisions to this geodatabase will occur biennially, with the next revision scheduled to be published in 2023. Resources in this dataset:Resource Title: LTAR Standard GIS Data Layers, 2020 version, File Geodatabase. File Name: LTAR_Standard_GIS_Layers_v2020.zipResource Description: This file geodatabase consists of authoritative GIS data layers of the Long-Term Agroecosystem Research Network. Data layers include: LTAR site locations, LTAR site points of contact and street addresses, LTAR experimental boundaries, LTAR site "legacy region" boundaries, LTAR eddy flux tower locations, and LTAR phenocam locations.Resource Software Recommended: ArcGIS,url: esri.com Resource Title: LTAR Standard GIS Data Layers, 2020 version, GeoJSON files. File Name: LTAR_Standard_GIS_Layers_v2020_GeoJSON_ADC.zipResource Description: The contents of the LTAR Standard GIS Data Layers includes geospatial data that represent locations and areas associated with the LTAR Network as of late 2020. This collection of geojson files includes spatial data describing LTAR site locations, addresses, experimental plots, fields and watersheds, eddy flux towers, and phenocams. There are six data layers in the geodatabase available to the public. This dataset was created in 2019-2020 by the LTAR network as a national collaborative effort among working groups and LTAR sites. Resource Software Recommended: QGIS,url: https://qgis.org/en/site/

These data are a point feature class that provides the location of Research and Development's offices across the United States.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 OGC WMS CSV Shapefile GeoJSON KML https://apps.fs.usda.gov/arcx/rest/services/EDW/EDW_ResearchStations_01/MapServer/0 http://data.fs.usda.gov/geodata/edw/datasets.php For complete information, please visit https://data.gov.

Today, deep neural networks are widely used in many computer vision problems, also for geographic information systems (GIS) data. This type of data is commonly used for urban analyzes and spatial planning. We used orthophotographic images of two residential districts from Kielce, Poland for research including urban sprawl automatic analysis with Transformer-based neural network application.Orthophotomaps were obtained from Kielce GIS portal. Then, the map was manually masked into building and building surroundings classes. Finally, the ortophotomap and corresponding classification mask were simultaneously divided into small tiles. This approach is common in image data preprocessing for machine learning algorithms learning phase. Data contains two original orthophotomaps from Wietrznia and Pod Telegrafem residential districts with corresponding masks and also their tiled version, ready to provide as a training data for machine learning models.Transformed-based neural network has undergone a training process on the Wietrznia dataset, targeted for semantic segmentation of the tiles into buildings and surroundings classes. After that, inference of the models was used to test model's generalization ability on the Pod Telegrafem dataset. The efficiency of the model was satisfying, so it can be used in automatic semantic building segmentation. Then, the process of dividing the images can be reversed and complete classification mask retrieved. This mask can be used for area of the buildings calculations and urban sprawl monitoring, if the research would be repeated for GIS data from wider time horizon.Since the dataset was collected from Kielce GIS portal, as the part of the Polish Main Office of Geodesy and Cartography data resource, it may be used only for non-profit and non-commertial purposes, in private or scientific applications, under the law "Ustawa z dnia 4 lutego 1994 r. o prawie autorskim i prawach pokrewnych (Dz.U. z 2006 r. nr 90 poz 631 z późn. zm.)". There are no other legal or ethical considerations in reuse potential.Data information is presented below.wietrznia_2019.jpg - orthophotomap of Wietrznia districtmodel's - used for training, as an explanatory imagewietrznia_2019.png - classification mask of Wietrznia district - used for model's training, as a target imagewietrznia_2019_validation.jpg - one image from Wietrznia district - used for model's validation during training phasepod_telegrafem_2019.jpg - orthophotomap of Pod Telegrafem district - used for model's evaluation after training phasewietrznia_2019 - folder with wietrznia_2019.jpg (image) and wietrznia_2019.png (annotation) images, divided into 810 tiles (512 x 512 pixels each), tiles with no information were manually removed, so the training data would contain only informative tilestiles presented - used for the model during training (images and annotations for fitting the model to the data)wietrznia_2019_vaidation - folder with wietrznia_2019_validation.jpg image divided into 16 tiles (256 x 256 pixels each) - tiles were presented to the model during training (images for validation model's efficiency); it was not the part of the training datapod_telegrafem_2019 - folder with pod_telegrafem.jpg image divided into 196 tiles (256 x 265 pixels each) - tiles were presented to the model during inference (images for evaluation model's robustness)Dataset was created as described below.Firstly, the orthophotomaps were collected from Kielce Geoportal (https://gis.kielce.eu). Kielce Geoportal offers a .pst recent map from April 2019. It is an orthophotomap with a resolution of 5 x 5 pixels, constructed from a plane flight at 700 meters over ground height, taken with a camera for vertical photos. Downloading was done by WMS in open-source QGIS software (https://www.qgis.org), as a 1:500 scale map, then converted to a 1200 dpi PNG image.Secondly, the map from Wietrznia residential district was manually labelled, also in QGIS, in the same scope, as the orthophotomap. Annotation based on land cover map information was also obtained from Kielce Geoportal. There are two classes - residential building and surrounding. Second map, from Pod Telegrafem district was not annotated, since it was used in the testing phase and imitates situation, where there is no annotation for the new data presented to the model.Next, the images was converted to an RGB JPG images, and the annotation map was converted to 8-bit GRAY PNG image.Finally, Wietrznia data files were tiled to 512 x 512 pixels tiles, in Python PIL library. Tiles with no information or a relatively small amount of information (only white background or mostly white background) were manually removed. So, from the 29113 x 15938 pixels orthophotomap, only 810 tiles with corresponding annotations were left, ready to train the machine learning model for the semantic segmentation task. Pod Telegrafem orthophotomap was tiled with no manual removing, so from the 7168 x 7168 pixels ortophotomap were created 197 tiles with 256 x 256 pixels resolution. There was also image of one residential building, used for model's validation during training phase, it was not the part of the training data, but was a part of Wietrznia residential area. It was 2048 x 2048 pixel ortophotomap, tiled to 16 tiles 256 x 265 pixels each.

Analysis of ‘2019 CT Data Catalog (GIS)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/168eaac6-5f52-4015-be99-93031db2fd0d on 26 January 2022.

--- Dataset description provided by original source is as follows ---

Catalog of high value data inventories produced by Connecticut executive branch agencies and compiled by the Office of Policy and Management, updated in 2019. This catalog contains information on high value GIS data only. A catalog of high value non-GIS data may be found at the following link: https://data.ct.gov/Government/2019-CT-Data-Catalog-Non-GIS-/f6rf-n3ke

As required by Public Act 18-175, executive branch agencies must annually conduct a high value data inventory to capture information about the high value data that they collect.

High value data is defined as any data that the department head determines (A) is critical to the operation of an executive branch agency; (B) can increase executive branch agency accountability and responsiveness; (C) can improve public knowledge of the executive branch agency and its operations; (D) can further the core mission of the executive branch agency; (E) can create economic opportunity; (F) is frequently requested by the public; (G) responds to a need and demand as identified by the agency through public consultation; or (H) is used to satisfy any legislative or other reporting requirements.

This dataset was last updated 2/3/2020 and will continue to be updated as high value data inventories are submitted to OPM.

The 2018 high value data inventories for Non-GIS and GIS data can be found at the following links: CT Data Catalog (Non GIS): https://data.ct.gov/Government/CT-Data-Catalog-Non-GIS-/ghmx-93jn/ CT Data Catalog (GIS): https://data.ct.gov/Government/CT-Data-Catalog-GIS-/p7we-na27 Less

--- Original source retains full ownership of the source dataset ---

EXXON Valdez Oil Spill (EVOS) data were generated by the Nation Marine Fishery Service (NMFS). The EVOS area includes Prince William Sound and adjacent coastal areas. The data were put on a CD-ROM with EVOS Geographic Information Systems (GIS) database, data dictionary, and bibliography. Data are related to oil spill clean up, damage assessments, and restoration efforts. Data sets include physical features, biological features, cultural features, land status, boundaries, place names, human use, shoreline oiling, surface oiling, hydrocarbon analysis, EVOS research areas, and miscellaneous.

Deprecation notice: This tool is deprecated because this functionality is now available with out-of-the-box tools in ArcGIS Pro. The tool author will no longer be making further enhancements or fixing major bugs.Use Add GTFS to a Network Dataset to incorporate transit data into a network dataset so you can perform schedule-aware analyses using the Network Analyst tools in ArcMap.After creating your network dataset, you can use the ArcGIS Network Analyst tools, like Service Area and OD Cost Matrix, to perform transit/pedestrian accessibility analyses, make decisions about where to locate new facilities, find populations underserved by transit or particular types of facilities, or visualize the areas reachable from your business at different times of day. You can also publish services in ArcGIS Server that use your network dataset.The Add GTFS to a Network Dataset tool suite consists of a toolbox to pre-process the GTFS data to prepare it for use in the network dataset and a custom GTFS transit evaluator you must install that helps the network dataset read the GTFS schedules. A user's guide is included to help you set up your network dataset and run analyses.Instructions:Download the tool. It will be a zip file.Unzip the file and put it in a permanent location on your machine where you won't lose it. Do not save the unzipped tool folder on a network drive, the Desktop, or any other special reserved Windows folders (like C:\Program Files) because this could cause problems later.The unzipped file contains an installer, AddGTFStoaNetworkDataset_Installer.exe. Double-click this to run it. The installation should proceed quickly, and it should say "Completed" when finished.Read the User's Guide for instructions on creating and using your network dataset.System requirements:ArcMap 10.1 or higher with a Desktop Standard (ArcEditor) license. (You can still use it if you have a Desktop Basic license, but you will have to find an alternate method for one of the pre-processing tools.) ArcMap 10.6 or higher is recommended because you will be able to construct your network dataset much more easily using a template rather than having to do it manually step by step. This tool does not work in ArcGIS Pro. See the User's Guide for more information.Network Analyst extensionThe necessary permissions to install something on your computer.Data requirements:Street data for the area covered by your transit system, preferably data including pedestrian attributes. If you need help preparing high-quality street data for your network, please review this tutorial.A valid GTFS dataset. If your GTFS dataset has blank values for arrival_time and departure_time in stop_times.txt, you will not be able to run this tool. You can download and use the Interpolate Blank Stop Times tool to estimate blank arrival_time and departure_time values for your dataset if you still want to use it.Help forum

This data was prepared as input for the Selkie GIS-TE tool. This GIS tool aids site selection, logistics optimization and financial analysis of wave or tidal farms in the Irish and Welsh maritime areas. Read more here: https://www.selkie-project.eu/selkie-tools-gis-technoeconomic-model/

This research was funded by the Science Foundation Ireland (SFI) through MaREI, the SFI Research Centre for Energy, Climate and the Marine and by the Sustainable Energy Authority of Ireland (SEAI). Support was also received from the European Union's European Regional Development Fund through the Ireland Wales Cooperation Programme as part of the Selkie project.

File Formats

Results are presented in three file formats:

tif Can be imported into a GIS software (such as ARC GIS) csv Human-readable text format, which can also be opened in Excel png Image files that can be viewed in standard desktop software and give a spatial view of results

Input Data

All calculations use open-source data from the Copernicus store and the open-source software Python. The Python xarray library is used to read the data.

Hourly Data from 2000 to 2019

• Wind - Copernicus ERA5 dataset 17 by 27.5 km grid
  10m wind speed

• Wave - Copernicus Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis dataset 3 by 5 km grid

Accessibility

The maximum limits for Hs and wind speed are applied when mapping the accessibility of a site.
The Accessibility layer shows the percentage of time the Hs (Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis) and wind speed (ERA5) are below these limits for the month.

Input data is 20 years of hourly wave and wind data from 2000 to 2019, partitioned by month. At each timestep, the accessibility of the site was determined by checking if
the Hs and wind speed were below their respective limits. The percentage accessibility is the number of hours within limits divided by the total number of hours for the month.

Environmental data is from the Copernicus data store (https://cds.climate.copernicus.eu/). Wave hourly data is from the 'Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis' dataset.
Wind hourly data is from the ERA 5 dataset.

Availability

A device's availability to produce electricity depends on the device's reliability and the time to repair any failures. The repair time depends on weather
windows and other logistical factors (for example, the availability of repair vessels and personnel.). A 2013 study by O'Connor et al. determined the
relationship between the accessibility and availability of a wave energy device. The resulting graph (see Fig. 1 of their paper) shows the correlation between accessibility at Hs of 2m and wind speed of 15.0m/s and availability. This graph is used to calculate the availability layer from the accessibility layer.

The input value, accessibility, measures how accessible a site is for installation or operation and maintenance activities. It is the percentage time the
environmental conditions, i.e. the Hs (Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis) and wind speed (ERA5), are below operational limits.
Input data is 20 years of hourly wave and wind data from 2000 to 2019, partitioned by month. At each timestep, the accessibility of the site was determined
by checking if the Hs and wind speed were below their respective limits. The percentage accessibility is the number of hours within limits divided by the total
number of hours for the month. Once the accessibility was known, the percentage availability was calculated using the O'Connor et al. graph of the relationship between the two. A mature technology reliability was assumed.

Weather Window

The weather window availability is the percentage of possible x-duration windows where weather conditions (Hs, wind speed) are below maximum limits for the
given duration for the month.

The resolution of the wave dataset (0.05° × 0.05°) is higher than that of the wind dataset
(0.25° x 0.25°), so the nearest wind value is used for each wave data point. The weather window layer is at the resolution of the wave layer.

The first step in calculating the weather window for a particular set of inputs (Hs, wind speed and duration) is to calculate the accessibility at each timestep.
The accessibility is based on a simple boolean evaluation: are the wave and wind conditions within the required limits at the given timestep?

Once the time series of accessibility is calculated, the next step is to look for periods of sustained favourable environmental conditions, i.e. the weather
windows. Here all possible operating periods with a duration matching the required weather-window value are assessed to see if the weather conditions remain
suitable for the entire period. The percentage availability of the weather window is calculated based on the percentage of x-duration windows with suitable
weather conditions for their entire duration.The weather window availability can be considered as the probability of having the required weather window available
at any given point in the month.

Extreme Wind and Wave

The Extreme wave layers show the highest significant wave height expected to occur during the given return period. The Extreme wind layers show the highest wind speed expected to occur during the given return period.

To predict extreme values, we use Extreme Value Analysis (EVA). EVA focuses on the extreme part of the data and seeks to determine a model to fit this reduced
portion accurately. EVA consists of three main stages. The first stage is the selection of extreme values from a time series. The next step is to fit a model
that best approximates the selected extremes by determining the shape parameters for a suitable probability distribution. The model then predicts extreme values
for the selected return period. All calculations use the python pyextremes library. Two methods are used - Block Maxima and Peaks over threshold.

The Block Maxima methods selects the annual maxima and fits a GEVD probability distribution.

The peaks_over_threshold method has two variable calculation parameters. The first is the percentile above which values must be to be selected as extreme (0.9 or 0.998). The second input is the time difference between extreme values for them to be considered independent (3 days). A Generalised Pareto Distribution is fitted to the selected
extremes and used to calculate the extreme value for the selected return period.

Data set that contains information on archaeological remains of the pre historic settlement of the Letolo valley on Savaii on Samoa. It is built in ArcMap from ESRI and is based on previously unpublished surveys made by the Peace Corps Volonteer Gregory Jackmond in 1976-78, and in a lesser degree on excavations made by Helene Martinsson Wallin and Paul Wallin. The settlement was in use from at least 1000 AD to about 1700- 1800. Since abandonment it has been covered by thick jungle. However by the time of the survey by Jackmond (1976-78) it was grazed by cattle and the remains was visible. The survey is at file at Auckland War Memorial Museum and has hitherto been unpublished. A copy of the survey has been accessed by Olof Håkansson through Martinsson Wallin and Wallin and as part of a Masters Thesis in Archeology at Uppsala University it has been digitised.

Olof Håkansson has built the data base structure in the software from ESRI, and digitised the data in 2015 to 2017. One of the aims of the Masters Thesis was to discuss hierarchies. To do this, subsets of the data have been displayed in various ways on maps. Another aim was to discuss archaeological methodology when working with spatial data, but the data in itself can be used without regard to the questions asked in the Masters Thesis. All data that was unclear has been removed in an effort to avoid errors being introduced. Even so, if there is mistakes in the data set it is to be blamed on the researcher, Olof Håkansson. A more comprehensive account of the aim, questions, purpose, method, as well the results of the research, is to be found in the Masters Thesis itself. Direkt link http://uu.diva-portal.org/smash/record.jsf?pid=diva2%3A1149265&dswid=9472

Purpose:

The purpose is to examine hierarchies in prehistoric Samoa. The purpose is further to make the produced data sets available for study.

Prehistoric remains of the settlement of Letolo on the Island of Savaii in Samoa in Polynesia

Database created for replication of GeoStoryTelling. Our life stories evolve in specific and contextualized places. Although our homes may be our primarily shaping environment, our homes are themselves situated in neighborhoods that expose us to the immediate “real world” outside home. Indeed, the places where we are currently experiencing, and have experienced life, play a fundamental role in gaining a deeper and more nuanced understanding of our beliefs, fears, perceptions of the world, and even our prospects of social mobility. Despite the immediate impact of the places where we experience life in reaching a better understanding of our life stories, to date most qualitative and mixed methods researchers forego the analytic and elucidating power that geo-contextualizing our narratives bring to social and health research. From this view then, most research findings and conclusions may have been ignoring the spatial contexts that most likely have shaped the experiences of research participants. The main reason for the underuse of these geo-contextualized stories is the requirement of specialized training in geographical information systems and/or computer and statistical programming along with the absence of cost-free and user-friendly geo-visualization tools that may allow non-GIS experts to benefit from geo-contextualized outputs. To address this gap, we present GeoStoryTelling, an analytic framework and user-friendly, cost-free, multi-platform software that enables researchers to visualize their geo-contextualized data narratives. The use of this software (available in Mac and Windows operative systems) does not require users to learn GIS nor computer programming to obtain state-of-the-art, and visually appealing maps. In addition to providing a toy database to fully replicate the outputs presented, we detail the process that researchers need to follow to build their own databases without the need of specialized external software nor hardware. We show how the resulting HTML outputs are capable of integrating a variety of multi-media inputs (i.e., text, image, videos, sound recordings/music, and hyperlinks to other websites) to provide further context to the geo-located stories we are sharing (example https://cutt.ly/k7X9tfN). Accordingly, the goals of this paper are to describe the components of the methodology, the steps to construct the database, and to provide unrestricted access to the software tool, along with a toy dataset so that researchers may interact first-hand with GeoStoryTelling and fully replicate the outputs discussed herein. Since GeoStoryTelling relied on OpenStreetMap its applications may be used worldwide, thus strengthening its potential reach to the mixed methods and qualitative scientific communities, regardless of location around the world. Keywords: Geographical Information Systems; Interactive Visualizations; Data StoryTelling; Mixed Methods & Qualitative Research Methodologies; Spatial Data Science; Geo-Computation.

NLEAP GIS 5.0 can help users identify hot spots across the landscape and identify management practices that can increase nitrogen use efficiency. A Nitrogen Trading Tool (NTT) analysis can be conducted to determine the potential benefits of implementing best management practices and the quantity of nitrogen savings that could potentially be traded in future air or water quality markets. Resources in this dataset:Resource Title: NLEAP GIS 5.0. File Name: Web Page, url: https://www.ars.usda.gov/research/software/download/?softwareid=428&modecode=30-12-30-15 download page

This record contains the data used by the North Slope Science Initiative (NSSI) scenario process. These data sets are listed in the Alaska DataCatalog. The data sets are grouped thematically and can be downloaded along with the DataCatalog using the links below. The NSSI Scenarios reports can be downloaded from a separate listing using the link below. The North Slope Science Initiation (NSSI) commissioned a scenario project as a means to provide NSSI member agencies with guidance for moving forward on implementing research and monitoring recommendations and priorities. The NSSI partnered with a research consortium, formed by the University of Alaska Fairbanks and GeoAdaptive, LLC, a scenario-specialist consulting group, to develop the Scenarios Project. These scenarios for energy and resource development helped envision the potential future state of the socio-ecological systems of the North Slope and adjacent seas, and can thereby inform and help resource management agencies to develop appropriate research and monitoring strategies for the future. The scenarios identified through this collaborative effort reflect a plausible range of potential future conditions in the region through 2040. However, these scenarios do not represent a development plan for the region; they were designed to be used as the basis for discussion on the future of the region to help best identify future research and monitoring priorities. As noted above, while the outcomes of this project provide a powerful and widely vetted tool to inform research and monitoring priorities, they do not set those priorities for NSSI or its individual member entities. This is not a policy document.

This project will adapt an existing computer model for assessing the suitability of a site for construction of a living shoreline, apply the model to Perdido Bay/Wolf Bay/Ono Island complex in coastal Alabama; Lake Pontchartrain, Louisiana; and Galveston Bay, Texas, and develop an interactive decision support tool that allows for a rapid assessment of a site.

Analysis of ‘PLACES: Census Tract Data (GIS Friendly Format), 2020 release’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/36454ff3-3bd6-4626-8607-ed62ff3f4619 on 12 February 2022.

--- Dataset description provided by original source is as follows ---

This dataset contains model-based census tract level estimates for the PLACES project 2020 release in GIS-friendly format. The PLACES project is the expansion of the original 500 Cities project and covers the entire United States—50 states and the District of Columbia (DC)—at county, place, census tract, and ZIP Code tabulation Areas (ZCTA) levels. It represents a first-of-its kind effort to release information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2018 or 2017 data, Census Bureau 2010 population estimates, and American Community Survey (ACS) 2014-2018 or 2013-2017 estimates. The 2020 release uses 2018 BRFSS data for 23 measures and 2017 BRFSS data for 4 measures (high blood pressure, taking high blood pressure medication, high cholesterol, and cholesterol screening). Four measures are based on the 2017 BRFSS data because the relevant questions are only asked every other year in the BRFSS. These data can be joined with the census tract 2015 boundary file in a GIS system to produce maps for 27 measures at the census tract level. An ArcGIS Online feature service is also available at https://www.arcgis.com/home/item.html?id=8eca985039464f4d83467b8f6aeb1320 for users to make maps online or to add data to desktop GIS software.

--- Original source retains full ownership of the source dataset ---