This map shows the location of multi-generational households in the United States in 2010. A multigenerational household is a household in with three or more generations reside within a single household. This is shown by using color to represent the count of multigenerational households as a percentage of total households. The size of the symbols represent the count of all multigenerational households within an area.The map shows this pattern for states, counties, tracts, and block groups. There is increasing geographic detail as you zoom in, and only one geography is configured to show at any time. The data source is the US Census Bureau, and the vintage is 2010. The original service and data metadata can be found here.

Layer references: Predominant Generations in the United States in 2018-------------------------------------------------------------------------------------------------------------------------------------This layer shows the predominant generations that make up the population of the United States using country to block group geographies. The vintage of the data is 2018. The top 3 predominant generations are Baby Boomers (born 1946-1964), Millennials (born 1981-1998), and Generation Z (born 1999-2016). Of these three, the predominant generation of the United States is Millennials. The popup is configured to show the predominant generation and population counts for each of the six generations. Size represents the total sum of categories (i.e. total population). Web Map: Predominant Generations in the United StatesFor more information, visit the Updated Demographics documentation. For a full list of variables, click the Data tab. Note: This layer will not being continuously updated or maintained.

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.

Power plant energy 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 generation from the CEC-licensed (jurisdictional) natural gas power plants and non-jurisdictional natural gas plants. Counties without symbols had no natural gas plants. Data is from 2022 and is current as of July 28, 2023.

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.

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 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.

This app highlights the predominant generations that make up the population of the United States, using country to block group geographies that vary according to zoom level. The map, which was featured in Esri's Living Atlas gallery, forms the basis of an analysis of travel agency locations to determine the best one for launching luxury travel services to baby boomers. Use the Bookmarks tool to see the predominant generations in the census tract areas in and around Los Angeles, Chicago, and Washington, D.C. Click individual census tracts on the map to see pop-up information, including the population of each generation in a given census tract.This app was created for instructional purposes only and should not be used as an authoritative resource.

The geospatial products described and distributed here depict the probability of high-severity fire, if a fire were to occur, for several ecoregions in the contiguous western US. The ecological effects of wildland fire � also termed the fire severity � are often highly heterogeneous in space and time. This heterogeneity is a result of spatial variability in factors such as fuel, topography, and climate (e.g. mean annual temperature). However, temporally variable factors such as daily weather and climatic extremes (e.g. an unusually warm year) also may play a key role. Scientists from the US Forest Service Rocky Mountain Research Station and the University of Montana conducted a study in which observed data were used to produce statistical models describing the probability of high severity fire as a function of fuel, topography, climate, and fire weather. Observed data from over 2000 fires (from 2002-2015) were used to build individual models for each of 19 ecoregions in the contiguous US (see Parks et al. 2018, Figure 1). High severity fire was measured using a fire severity metric termed the relativized burn ratio, which uses pre- and post-fire Landsat imagery to measure fire-induced ecological change. Fuel included pre-fire metrics of live fuel amount such as NDVI. Topography included factors such as slope and potential solar radiation. Climate summarized 30-year averages of factors such as mean summer temperature that spatially vary across the study area. Lastly, fire weather incorporated temporally variable factors such as daily and annual temperature. In turn, these statistical models were used to generate 'wall-to-wall' maps depicting the probability of high severity fire, if a fire were to occur, for 13 of the 19 ecoregions. Maps were not produced for ecoregions in which model quality was deemed inadequate. All maps use fuel data representing the year 2016 and therefore provide a fairly up-to-date assessment of the potential for high severity fire. For those ecoregions in which the relative influence of fire weather was fairly strong (n=6), two additional maps were produced, one depicting the probability of high severity fire under moderate weather and the other under extreme weather. An important consideration is that only pixels defined as forest were used to build the models; consequently maps exclude pixels considered non-forest.

Map scientific evidence on the generational diversity in Nursing Practice Environments.

Energy generation data and map are from the California Energy Commission. Map depicts utility scale power plants (with nameplate capacity of 1 MW or more). Hydroelectric plants are designated as a renewable energy source if their nameplate capacity is 30 MW or less. Renewables include Biomass, Geothermal, Solar Thermal, Solar Photovoltaic, Small Hydroelectric, and Wind. Counties without symbols had no utility-scale plants. Data is from 2021 and is current as of October 27, 2022. For more information, contact Rebecca Vail at Rebecca.Vail@energy.ca.gov or John Hingtgen at John.Hingtgen@energy.ca.gov.

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.

On behalf of the Press and Information Office of the Federal Government, the opinion research institute Kantar conducted a target group survey of the ´Generation Z´. For this purpose, 1,022 people between the ages of 14 and 24 were surveyed online between 05 and 18 July 2021. The focus of the survey was on the values and orientation of the generation, their situation in the pandemic, political interest and information behaviour as well as political and social attitudes. In order to map the influence of the corona pandemic on the attitudes and social image of Generation Z, the results of this survey were compared with a survey from 2019.
1. Current life circumstances: life satisfaction; highest school-leaving qualification of father and mother; material situation: frequency of renunciation for financial reasons; source of money (from own work, from parents, from state support, from elsewhere); primary source of money; negative effects of the Corona crisis on personal income; organisation of distance learning (communication via a digital learning platform, via video conference, via e-mail, via messenger/chats such as e.g. WhatsApp, via a cloud, by telephone, by post or by other means); agreement with statements on the situation in schools/colleges (I was able to concentrate well on my tasks at home, I missed direct contact with my classmates/ fellow students, my grades deteriorated during the pandemic, distance learning at my school/college worked well, I had insufficient equipment to follow lessons, the accessibility of teachers was very good even in times of distance learning, learning became more strenuous for me during the pandemic); opinion on the future recognition of school, university or professional degrees made during the Corona pandemic; leisure activities during the pandemic (less sport since the beginning of the pandemic than before, relationships with friends have deteriorated during the pandemic, significantly more time on the internet since the beginning of the pandemic than before, started a new hobby during the pandemic); vaccination status; likelihood of Corona vaccination.

1. Values and attitudes: personally most important life goals (e.g. self-discovery, independence, enjoying life, career, etc.); importance of various aspects for pursuing a profession (secure job, adequate income, interesting work that is fun, compatibility of private life and profession (work-life balance), career opportunities, responsibility, opportunities for further training and development); comparison of values : comparison of values Corona: extensive collection of data for infection protection vs. data protection, especially young vs. especially old people have suffered from the pandemic, pandemic as a chance for change vs. after the pandemic back to the usual normality, comparison of values State: debts in favour of education and infrastructure not a problem vs. always a burden for future generations, active role of the state for important future tasks such as climate protection and educational justice vs. leaving a passive role and shaping of the future to society and the economy, orienting politics towards future generations vs. protecting the interests of those who have already made a contribution to society, comparison of lifestyle values: conscious renunciation in favour of sustainability vs. doing what I feel like doing, doing without in favour of health vs. having fun in the foreground, self-realisation vs. putting aside one´s own needs in favour of one´s personal environment, today´s generation has completely different values than the generation before it vs. in principle very similar values as the generation before it).

2. Media and information: interest in politics; points of contact with politics in everyday life (e.g. media consumption, when using social networks, in personal conversations with friends and family, at work, at school or university, in public spaces, in leisure time/hobbies); being informed about politics; most frequently used sources of political information (media) (e.g. news programmes on TV, talk shows on TV, websites of public institutions and authorities, etc.). e.g. news programmes on TV, talk shows on TV, websites of public institutions and authorities, satire programmes on TV, etc.); change in political information behaviour in the Corona pandemic.

3. Politics and society: satisfaction with democracy; opinion on democracy as an idea; need for reform of politics in Germany; most important political problems in Germany (open); satisfaction with the work of the federal government; trust in institutions (judiciary, environmental and aid organisations such as Greenpeace or Amnesty International, public health authorities such as the Robert Koch Institute, federal government, Bundestag, police, churches, school/university); perception of social lines of conflict (between rich and poor, employers and employees, young and old, foreigners and Germans, East Germans and West...

There is no description available for this dataset.

Spatial coverage index compiled by East View Geospatial of set "Soviet 1:200,000 Scale Geological Maps (First Generation) Explanatory Notes". Source data from VSEGEI (publisher). Type: Geoscientific - Explantory Notes. Scale: 1:200,000. Region: Asia, Former USSR.

NIDM Results

Collection description

Subject species

homo sapiens

Map type

T

Energy Data is collected for power plants that have a nameplate capacity of 1 MW or more. Counties without pie symbols had no utility-scale (commercial) electric generation installed. Distributed renewable generation (e.g. rooftop solar) is not included. Map and data originate from the California Energy Commission Quarterly Fuel and Energy Reports. Data is from 2020, and is current as of February 2022. For more information, please contact Rebecca Vail at (916) 477-0738 or John Hingtgen at (916) 510-9747

This map outlines the renewable sources of electrical generation in gigawatt-hours(GWh) for all counties in California for 2019. Sources below 1 megawatt (MW) were not included in this map. Counties without a symbol had no utility-scale (commercial) renewable electric generation installed. Data obtained from Quarterly Fuel and Energy Reports (QFER) and the Wind Performance Reporting System (WPRS) databases.

The "Distributed Generation SP Manweb Heat Maps - SPM Grid Substations" dataset provides an indication of SPEN’s network capabilities and potential opportunities to connect Distributed Generation (DG) to the 11kV, 33kV and 132kV network in the SP Manweb (SPM) licence area (covering Cheshire, Merseyside, North Shropshire, North Wales). Each substation and circuit are assigned one of the following categories:Green: All operational factors are within tolerable limits and so opportunities may exist to connect additional DG 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 DG 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 Triage : SPEN Data Triage Risk Assessments provide information and a detailed overview of how we approach the Data Triage process. The risk assessment will determine the dataset classification and whether it can be made available, and under which licence. Click below to view the Data Triage document for this dataset. These are hosted on our SP Energy Networks website and can be viewed by clicking here

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The aim of this study is twofold. The first aim (study 1a) is to assess (a) language characteristics, (b) cultural values, (c) national identification, and (d) media use in three generations of Turkish-Dutch and Dutch citizens. In doing so, we map the acculturation process for these four characteristics in three generations of Turkish-Dutch citizens, how they relate to the characteristics in three generations of Dutch citizens and specify where differences arise between generations of Turkish-Dutch citizens.

The second aim (study 1b) is to explore the message responses to mass-mediated health campaigns among the three generations of Turkish-Dutch and native Dutch citizens. In doing so, we explore (a) whether Dutch mass-mediated health campaigns result in different message responses in Turkish-Dutch (vs. Dutch) citizens across generations and (b) the extent to which language characteristics, cultural values, and national identification relate to these differences.