The Python code provided generates polygonal maps resembling geographical landscapes, where certain areas may represent features like lakes or inaccessible regions. These maps are generated with specified characteristics such as regularity, gap density, and gap scale.

Features:

1. Polygon Generation:

   • The code utilizes the Shapely library to generate polygonal shapes within specified bounding boxes. These polygons serve as the primary representation of the map.

2. Gap Generation:

   • Within the generated polygons, the code introduces gaps to simulate features like lakes or inaccessible areas. These gaps are represented as holes within the central polygon.
3. Forest Generation
   • Within the generated polygons, the code introduces different forest areas. These forest are added like a new Feature inside the GEOJSON.

4. Parameterized Generation:

   • The generation process is parameterized, allowing control over features such as regularity (shape uniformity), gap density (homogeneity of gaps), and gap scale (size of gaps relative to the polygon).

Components:

1. PolygonGenerator Class:

   • Responsible for generating the outer polygon shape and introducing gaps to simulate features.
   • Offers methods to generate individual polygons with specified characteristics.

2. Parameter Ranges and Experimentation:

   • The code includes predefined ranges for regularity, gap density, vertex number, bounding box, forest density and forest scale range in 3 different CSV.
   • It conducts experiments by generating maps with different parameter combinations, offering insights into how these parameters affect the map's appearance.

Usage:

1. Map Generation:

   • Users can instantiate the PolygonGenerator class to generate individual polygons representing maps with specific features.
   • Parameters such as regularity, gap density, and gap scale can be adjusted to customize the map generation process.

2. Experimentation:

   • Users can experiment with different parameter combinations to observe the effects on map generation.
   • This allows for exploration and understanding of how different parameters influence the characteristics of generated maps.

Potential Applications:

• The code can be used in various applications requiring the generation of simulated landscapes, such as in gaming, geographical analysis, or educational tools.
• It provides a flexible and customizable framework for creating maps with specific features, allowing users to tailor the generated maps to their requirements.
• Can be applied to generate maps for drone scanning operations, facilitating optimized area division and efficient data collection.

The "Transmission Generation Heat Map" dataset provides an indication of the potential opportunities (or constraints) to connect to SPEN's transmission network by detailing all connected and contracted projects. This allows potential customers to have an interactive representation of the network and view the type of projects connected to specific substations within the SPEN Transmission area.DisclaimerWhilst all reasonable care has been taken in the preparation of this data, SP Energy Networks does not accept any responsibility or liability for the accuracy or completeness of this data, and is not liable for any loss that may be attributed to the use of this data. For the avoidance of doubt, this data should not be used for safety critical purposes without the use of appropriate safety checks and services e.g. LineSearchBeforeUDig etc. This heatmap will be updated on a monthly basis using the published data from the ESO's TEC register, the latest SPEN ECR and the contrated demand data to ensure we have an accurate representation of projects the ESO has considered as connected and/or contracted. It is important to note, our refresh of this data won't always be aligned to the latest available version of the ESO TEC register. Therefore, there may be small discrepancies between the two datasets. For the most up-to-date version of this data, please visit the ESO TEC register. Data TriageAs part of our commitment to enhancing the transparency, and accessibility of the data we share, we publish the results of our Data Triage process.Our Data Triage documentation includes our Risk Assessments; detailing any controls we have implemented to prevent exposure of sensitive information. Click here to access the Data Triage documentation for the Transmission Generation Heat Map dataset. To access our full suite of Data Triage documentation, visit the SP Energy Networks Data & Information.Download dataset metadata (JSON)

This Web Map Service depicts the location of clean electricity generating facilities by type of clean energy source and power generation capacity. Clean energy sources shown on the map include biomass, hydro, nuclear, solar, tidal and wind. The data comes from the provinces and territories, other federal departments and clean energy associations in Canada. The service is one of many themes mapped in the web mapping application Map of Clean Energy Resources and Projects (CERP) in Canada.

The "Transmission Generation Heat Map" dataset provides an indication of the potential opportunities (or constraints) to connect to SPEN's transmission network by detailing all connected and contracted projects. This allows potential customers to have an interactive representation of the network and view the type of projects connected to specific substations within the SPEN Transmission area.For additional information on column definitions, please click on the Dataset schema link below. Disclaimer: This heat map will be updated on a monthly basis using the published data from the ESO's TEC register to ensure we have an accurate representation of projects the ESO has considered as connected and/or contracted. It is important to note, our refresh of this data won't always be aligned to the latest available version of the ESO TEC register. Therefore, there may be small discrepancies between the two datasets. For the most up-to-date version of this data, please visit https://data.nationalgrideso.com/connection-registers/transmission-entry-capacity-tec-registerNote: A formatted copy of this dataset can be downloaded from the Export tab under Alternative exports.If you wish to provide feedback at a dataset or row level, please click on the “Feedback” tab aboveData TriageAs part of our commitment to enhancing the transparency, and accessibility of the data we share, we publish the results of our Data Triage process.Our Data Triage documentation includes our Risk Assessments; detailing any controls we have implemented to prevent exposure of sensitive information. Click here to access the Data Triage documentation for the Transmission Generation Heat Map dataset. To access our full suite of Data Triage documentation, visit the SP Energy Networks Data & Information.Download dataset metadata (JSON)

Energy generation data and map are from the California Energy Commission. Map depicts commercial geothermal energy generation by county. Unshaded counties had no commercial geothermal energy generation. Data is from 2022 and is current as of May 14, 2024. Projection: NAD 1983 (2011) California Teale) Albers (Meters). For more information, contact John Hingtgen at 916 510-9747 or Jessica Lin at 415 990-8392

The product data are six statistics that were estimated for the chemical concentration of lithium in the soil C horizon of the conterminous United States. The estimates are made at 9998 locations that are uniformly distributed across the conterminous United States. The six statistics are the mean for the isometric log-ratio transform of the concentrations, the equivalent mean for the concentrations, the standard deviation for the isometric log-ratio transform of the concentrations, the probability of exceeding a concentration of 55 milligrams per kilogram, the 0.95 quantile for the isometric log-ratio transform of the concentrations, and the equivalent 0.95 quantile for the concentrations. Each statistic may be used to generate a statistical map that shows an attribute of the distribution of lithium concentration.

MAGPIE is an interactive tool to help users (e.g., professional coders, researchers, clinicians) find SNOMED CT and ICD-10-PCS codes for medical procedures and interventions. MAGPIE is an NLM research tool (it has not been tested in a production environment), and is developed primarily for the 2018 version of ICD-10-PCS. MAGPIE combines lexical and map-assisted searching strategies to look for SNOMED CT, ICD-9-CM, and ICD-10-PCS codes sequentially.

This map contains NYC administrative boundaries enriched with various demographics datasets.Learn more about Esri's Enrich Layer / Geoenrichment analysis tool.Learn more about Esri's Demographics, Psychographic, and Socioeconomic datasets.Search for a specific location or site using the search bar. Toggle layer visibility with the layer list. Click on a layer to see more information about the feature.

The survey data shows that 20 to 29-year-olds in Poland generated the most significant amount of page views in the in the "Maps and Locators" category in April 2019. Among users in this age group, the average number of page views per user was 14.

In this context, we are sharing the data related to our article. We have made available the datasets used for training, the data employed to generate predictions in the test environment, the outcomes of our experiments, and the code that facilitated the evaluation of the data.

This data table provides the detailed data quality assessment scores for the Transmission Generation Heat Map. The quality assessment was carried out on the 31st of March. At SPEN, we are dedicated to sharing high-quality data with our stakeholders and being transparent about its' quality. This is why we openly share the results of our data quality assessments. We collaborate closely with Data Owners to address any identified issues and enhance our overall data quality. To demonstrate our progress we conduct, at a minimum, bi-annual assessments of our data quality - for datasets that are refreshed more frequently than this, please note that the quality assessment may be based on an earlier version of the dataset. To learn more about our approach to how we assess data quality, visit Data Quality - SP Energy Networks. We welcome feedback and questions from our stakeholders regarding this process. Our Open Data Team is available to answer any enquiries or receive feedback on the assessments. You can contact them via our Open Data mailbox at opendata@spenergynetworks.co.uk.The first phase of our comprehensive data quality assessment measures the quality of our datasets across three dimensions. Please refer to the dataset schema for the definitions of these dimensions. We are now in the process of expanding our quality assessments to include additional dimensions to provide a more comprehensive evaluation and will update the data tables with the results when available.

The "Distributed Generation SP Distribution Heat Maps - SPD Primary" dataset provides an indication of SPEN’s network capabilities and potential opportunities to connect Distributed Generation (DG) to the 11kV and 33kV network for the SP Distribution (SPD) licence area (covering Central & Southern Scotland).Each substation and circuit are assigned to one of the following categories:Green: All operational factors are within tolerable limits and so opportunities may exist to connect additional Distributed Generation without reinforcing the network (subject to detailed studies).Amber: At least one factor is nearing its operational limit and hence, depending on the nature of the application, network reinforcement may be required. However, this can only be confirmed by detailed network analysis.Red: At least one factor is close to its operational limit and so installation of most levels of Distributed Generation and a local connection is highly unlikely. It may also require extensive reinforcement works or given the lack of a local connection, require an extensive amount of sole user assets to facilitate such a connection.For additional information on column definitions, please click on the Dataset schema link below.Disclaimer: Whilst all reasonable care has been taken in the preparation of the information and data presented within these pages, SP Energy Networks is not responsible for any loss that may be attributed to the use of the data.Download dataset metadata (JSON)If you wish to provide feedback at a dataset or row level, please click on the “Feedback” tab above.Data TriageAs part of our commitment to enhancing the transparency, and accessibility of the data we share, we publish the results of our Data Triage process.Our Data Triage documentation includes our Risk Assessments; detailing any controls we have implemented to prevent exposure of sensitive information. Click here to access the Data Triage documentation for the Distributed Generation Heat Maps dataset. To access our full suite of Data Triage documentation, visit the SP Energy Networks Data & Information.

Power plant capacity data and map are from the California Energy Commission.The CEC licenses thermal power plants 50 megawatts (MW) and greater andthe infrastructure serving the plants such as electric transmission lines, fuelsupply lines, and water pipelines. These licensed plants are referred to asjurisdictional plants. This map depicts the capacity of CEC-licensed(jurisdictional) natural gas power plants and non-jurisdictional natural gasplants. Counties without symbols had no natural gas power plants. Data is from2024 and is current as of May 29, 2025. Projection: NAD 1983 (2011) California(Teale) Albers (Meters). For more information, contact John Hingtgen atjohn.hingtgen@energy.ca.gov.

Power plant capacity data and map are from the California Energy Commission. The CEC licenses thermal power plants 50 megawatts (MW) and greater and the infrastructure serving the plants such as electric transmission lines, fuel supply lines, and water pipelines. These licensed plants are referred to as jurisdictional plants. This map depicts the capacity of CEC-licensed (jurisdictional) natural gas power plants and non-jurisdictional natural gas plants. Counties without symbols had no natural gas power plants. Data is from 2022 and is current as of June 23, 2022. Projection: NAD 1983 (2011) California (Teale) Albers (Meters). For more information, contact Gordon Huang at (916) 477-0738 or John Hingtgen at (916) 510-9747.

Transmission lines metadata:Based on the HSIP Gold 2013 power transmission lines data. The HSIP data was clipped to California and then dissolved on the fields BUS_NAME and VOLT_CLASS. This information was provided by calema_gis on ArcGIS Online.Hydroelectric power plants metadata:Operable electric generating plants in the United States by energy source. This includes all plants that are operating, on standby, or short- or long-term out of service with a combined nameplate capacity of 1 MW or more. Only hydroelectric power plants where displayed by creating a definition query. The sources of this information include EIA-860, Annual Electric Generator Report, EIA-860M, Monthly Update to the Annual Electric Generator Report and EIA-923, Power Plant Operations Report. This data was provided by the U.S. Energy Information Administration. For more information on this data or the U.S. Energy Information Administration, please use the following link:https://www.eia.gov/maps/layer_info-m.phpThe Transmission Lines and Hydroelectric Power Plants web map is a feature service used in the Sierra Nevada Cascade story map; therefore, it should not be altered or deleted under any circumstances while the story map is in use.

Data and model checkpoints for paper "Weakly Supervised Concept Map Generation through Task-Guided Graph Translation" by Jiaying Lu, Xiangjue Dong, and Carl Yang. The paper has been accepted by IEEE Transactions on Knowledge and Data Engineering (TKDE).

GT-D2G-*.tar.gz are model checkpoints for GT-D2G variants. These models are trained by seed=27. nyt/dblp/yelp.*.win5.pickle.gz are initial graphs generated by NLP pipelines. glove.840B.restaurant.400d.vec.gz is the pre-trained embedding for the Yelp dataset.

For more instructions, please refer to our GitHub repo.

This data (csv file) provides simulated hourly time series of existing offshore wind generation for the regions shown in the attached map. Only regions with existing (by the time of modeling) offshore wind power plants are simulated (otherwise the data are NaN). The map shows the resulting capacity factors (annual mean). Wake losses are modeled, with additional 5 % of other losses and unavailability considered. The time stamps are in GMT; the variable (column) names relate to the region names shown in the maps. The data include also country-level aggregations, e.g., UK00_OFF is the aggregated offshore wind generation of all the UK regions (weighted by regional installed capacities). The data are part of the variable renewable energy generation time series created for ENTSO-E in the 2021 update of the Pan-European Climate Database (PECD) dataset. ENTSO-E has used the data in ERAA 2021 and Winter Outlook 2021-2022 assessments, and they are used in TYNDP 2022. The simulations are carried out by DTU Wind Energy, with the future technology selection and data validation discussed and agreed with ENTSO-E and its members. The linked journal paper (1st link) describes the ERA5-based simulation methodology. It is requested that the paper is cited when the data are used. The linked related journal paper (2nd link) describes the modeling of wake losses and storm shutdown behaviour for the offshore wind power plants. This item is part of a larger collection of wind and solar data: https://doi.org/10.11583/DTU.c.5939581

Abstract:Monash University under commission of Geoscience Australia produced an offshore wind capacity factor map assessed at a 150m hub height applying the Bureau of Meteorology 10 year (2009-2018) “Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia” (BARRA) hindcast model. The wind capacity factor has been calculated using the bounding curve of all scaled power curves for wind turbines available within the Open Energy Platform as of 2021. Average wind capacity factor values were also calculated for the Vestas V126 3.45MW and the GE V130 3.2MW wind turbines and are available in this web map service.Lineage:The Monash University project report (Offshore wind capacity factor maps - evaluating Australia's offshore wind resources potential) which is associated to this metadata record, details the method used to produce the offshore wind capacity factor maps. The method included geospatial alignment of the raw data, wind speed interpolation at 150m, calculation of the mean and standard deviation for hourly wind speeds at 150m from 2009 to 2018, the application of the methods of moments technique to calculate the shape and scale parameter of the wind Weibull distribution and calculation of a bounding curve for the power curves of wind turbines.The maximum offshore wind generation potential was calculated through the generation of a bounding curve for the currently, as of 2021, wind turbine power curves within the Open Energy Platform. The Weibull distribution parameters and the bounding curve were then combined to calculate the wind capacity factor values.Average wind capacity factor values were also calculated for the Vestas V126 3.45MW and the GE V130 3.2MW wind turbines.© Commonwealth of Australia (Geoscience Australia) 2022.Downloads and Links:Web ServicesOffshore Wind Capacity Factor Maps (Map Server)Offshore wind Capacity Factor Maps (WMS)Downloads available from the expanded catalogue link, belowMetadata URL:https://pid.geoscience.gov.au/dataset/ga/146703

Spatial coverage index compiled by East View Geospatial of set "Russia 1:1,000,000 Scale Geological Maps (Third Generation)". Source data from VSEGEI (publisher). Type: Geoscientific - Geology. Scale: 1:1,000,000. Region: Asia, Former USSR.

Results File for MAPS stranded assets paper.