No Publication Abstract is Available

This dataset consists of summer temperature metrics for Boston, MA. These heat metrics summarize six CAPA Urban Heat Watch program temperature and heat index datasets using geographical boundaries from the Census Tract (CT) layer. Heat datasets were created by Museum of Science, Boston, and the Helmuth Lab at Northeastern University. Heat metrics are presented in the attribute table as mean values of each Heat Watch program dataset for all hexagon features. The six heat values included in this table are July 2019 temperature and heat index in degrees Fahrenheit for each of 3 1-hour periods -- 6 a.m., 3 p.m., and 7 p.m. EDT. The geographic boundaries used to summarize the heat metrics are current as of 2019.

The global 3D mapping and modeling market is expected to grow significantly in the next few years as demand increases for detailed and accurate representations of physical environments in three-dimensional space. Estimated to be valued at USD 38.62 billion in the year 2025, the market was expected to grow at a CAGR of 14.5% from 2025 to 2033 and was estimated to reach an amount of USD 90.26 billion by the end of 2033. The high growth rate is because of improvement in advanced technologies with the development of high-resolution sensors and methods of photogrammetry that make possible higher-resolution realistic and immersive 3D models.Key trends in the market are the adoption of virtual and augmented reality (VR/AR) applications, 3D mapping with smart city infrastructure, and increased architecture, engineering, and construction utilization of 3D models. Other factors are driving the growing adoption of cloud-based 3D mapping and modeling solutions. The solutions promise scalability, cost-effectiveness, and easy access to 3D data, thus appealing to business and organizations of all sizes. Recent developments include: Jun 2023: Nomoko (Switzerland), a leading provider of real-world 3D data technology, announced that it has joined the Overture Maps Foundation, a non-profit organization committed to fostering collaboration and innovation in the geospatial domain. Nomoko will collaborate with Meta, Amazon Web Services (AWS), TomTom, and Microsoft, to create interoperable, accessible 3D datasets, leveraging its real-world 3D modeling capabilities., May 2023: The Sanborn Map Company (Sanborn), an authority in 3D models, announced the development of a powerful new tool, the Digital Twin Base Map. This innovative technology sets a new standard for urban analysis, implementation of Digital Cities, navigation, and planning with a fundamental transformation from a 2D map to a 3D environment. The Digital Twin Base Map is a high-resolution 3D map providing unprecedented detail and accuracy., Feb 2023: Bluesky Geospatial launched the MetroVista, a 3D aerial mapping program in the USA. The service employs a hybrid imaging-Lidar airborne sensor to capture highly detailed 3D data, including 360-degree views of buildings and street-level features, in urban areas to create digital twins, visualizations, and simulations., Feb 2023: Esri, a leading global provider of geographic information system (GIS), location intelligence, and mapping solutions, released new ArcGIS Reality Software to capture the world in 3D. ArcGIS Reality enables site, city, and country-wide 3D mapping for digital twins. These 3D models and high-resolution maps allow organizations to analyze and interact with a digital world, accurately showing their locations and situations., Jan 2023: Strava, a subscription-based fitness platform, announced the acquisition of FATMAP, a 3D mapping platform, to integrate into its app. The acquisition adds FATMAP's mountain-focused maps to Strava's platform, combining with the data already within Strava's products, including city and suburban areas for runners and other fitness enthusiasts., Jan 2023: The 3D mapping platform FATMAP is acquired by Strava. FATMAP applies the concept of 3D visualization specifically for people who like mountain sports like skiing and hiking., Jan 2022: GeoScience Limited (the UK) announced receiving funding from Deep Digital Cornwall (DDC) to develop a new digital heat flow map. The DDC project has received grant funding from the European Regional Development Fund. This study aims to model the heat flow in the region's shallower geothermal resources to promote its utilization in low-carbon heating. GeoScience Ltd wants to create a more robust 3D model of the Cornwall subsurface temperature through additional boreholes and more sophisticated modeling techniques., Aug 2022: In order to create and explore the system's possibilities, CGTrader worked with the online retailer of dietary supplements Hello100. The system has the ability to scale up the generation of more models, and it has enhanced and improved Hello100's appearance on Amazon Marketplace.. Key drivers for this market are: The demand for 3D maps and models is growing rapidly across various industries, including architecture, engineering, and construction (AEC), manufacturing, transportation, and healthcare. Advances in hardware, software, and data acquisition techniques are making it possible to create more accurate, detailed, and realistic 3D maps and models. Digital twins, which are virtual representations of real-world assets or systems, are driving the demand for 3D mapping and modeling technologies for the creation of accurate and up-to-date digital representations.

. Potential restraints include: The acquisition and processing of 3D data can be expensive, especially for large-scale projects. There is a lack of standardization in the 3D mapping modeling industry, which can make it difficult to share and exchange data between different software and systems. There is a shortage of skilled professionals who are able to create and use 3D maps and models effectively.. Notable trends are: 3D mapping and modeling technologies are becoming essential for a wide range of applications, including urban planning, architecture, construction, environmental management, and gaming. Advancements in hardware, software, and data acquisition techniques are enabling the creation of more accurate, detailed, and realistic 3D maps and models. Digital twins, which are virtual representations of real-world assets or systems, are driving the demand for 3D mapping and modeling technologies for the creation of accurate and up-to-date digital representations..

This is a heatmap (a graphical representation of data where the individual values contained in a matrix are represented as colors) of 2013 deer hunt kills within the California Department of Fish & Wildlife (CDFW) North Central Region (Region 2). The data was compiled from 2013 CDFW Automated Licensing Data System (ALDS) tables. Text descriptions from hunters were approximated and placed with geographic coordinates. The resulting point data was converted to a heatmap using Kernel Density Tool in ArcGIS 10.1

description: This is a heatmap (a graphical representation of data where the individual values contained in a matrix are represented as colors) of 2013 deer hunt kills within the California Department of Fish & Wildlife (CDFW) North Central Region (Region 2). The data was compiled from 2013 CDFW Automated Licensing Data System (ALDS) tables. Text descriptions from hunters were approximated and placed with geographic coordinates. The resulting point data was converted to a heatmap using Kernel Density Tool in ArcGIS 10.1; abstract: This is a heatmap (a graphical representation of data where the individual values contained in a matrix are represented as colors) of 2013 deer hunt kills within the California Department of Fish & Wildlife (CDFW) North Central Region (Region 2). The data was compiled from 2013 CDFW Automated Licensing Data System (ALDS) tables. Text descriptions from hunters were approximated and placed with geographic coordinates. The resulting point data was converted to a heatmap using Kernel Density Tool in ArcGIS 10.1

This activity will no longer be maintained after June 16, 2025. Current lessons are available in the K-12 Classroom Activities Gallery.

This activity uses Map Viewer and is designed for intermediate users. We recommend MapMaker when getting started with maps in the classroom - see this StoryMap for the same activity in MapMaker.ResourcesMapTeacher guide Student worksheetVocabulary and puzzlesSelf-check questionsGet startedOpen the map.Use the teacher guide to explore the map with your class or have students work through it on their own with the worksheet.New to GeoInquiriesTM? See Getting to Know GeoInquiries.Social Studies standardsC3: D2.Geo.1.6-8 – Construct maps to represent and explain the spatial patterns of cultural and environmental characteristics. C3: D2.Geo.2.6-8 – Use maps, satellite images, photographs, and other representations to explain relationships between the locations of places and regions, as well as changes in their environmental characteristics.Learning outcomesDescribe the characteristic of yearly and monthly global temperature patterns.Analyze the effects of latitude, elevation, and proximity to the oceans on global temperature patterns. populations in the past 2,000 years.More activitiesAll World Geography GeoInquiriesAll GeoInquiries

This layer shows median household income by race and by age of householder. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Median income and income source is based on income in past 12 months of survey. This layer is symbolized to show median household income. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B19013B, B19013C, B19013D, B19013E, B19013F, B19013G, B19013H, B19013I, B19049, B19053Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

This dataset contains all variables that were used to calculate the physical heat cluster map of Hotterdam. Parent item: Hotterdam: Urban heat in Rotterdam and health effects Heat waves will occur in Rotterdam with greater frequency in the future. Those affected most will be the elderly – a group that is growing in size. In the light of the Paris heat wave of August 2003 and the one in Rotterdam in July 2006, mortality rates among the elderly in particular are likely to rise in the summer. The aim of the Hotterdam research project was to gain a better understanding of urban heat. Heat was measured and the surface energy balance modelled from that perspective. Social and physical features of the city were identified in detail with the help of satellite images, GIS and 3D models. The links between urban heat/surface energy balance and the social/physical features of Rotterdam were determined on the basis of multivariable regression analysis. The decisive features of the heat problem were then clustered and illustrated on a social and a physical heat map. The research project produced two heat maps, an atlas of underlying data.

Geothermics is the study of heat generated in Earth's interior and its manifestation at the surface. The National Geophysical Data Center (NGDC) has a variety of publications and data sets which provide information on the location, magnitude, and potential uses of geothermal resources. The publication, "Thermal Springs List for the United States" (1981) is a compilation of 1,700 thermal springs locations in 23 states. The list gives the geographic locations of thermal springs by state, and is sorted by degrees of latitude and longitude within the state. It contains the name of each spring (where available), maximum surface temperature (in both degrees Fahrenheit and degrees Celsius), name of corresponding USGS 1:2,500,000-scale (AMS) map, largest scale USGS topographic map coverage available (either 7.5 or 15-min. quadrangle), and cross-references. Thermal springs listed include natural surface hydrothermal features (springs, pools, mud pots, mud volcanoes, geysers, fumaroles, and steam vents) at temperatures of 20 degrees Celsius (68 degrees Fahrenheit) or higher. They do not include wells or mines, except at sites where they supplement or replace natural vents that have been active recently or at sites where orifices are indistinguishable as natural or artificial. The thermal springs data from this publication are also available on-line."Geothermal Gradient Map of the United States" (1982) shows 1,700 wells, with accompanying heat flow and conductivity data. This map was produced in cooperation with Los Alamos National Laboratory. Thermal aspect data (1991) from the Decade of North American Geology project, are available on diskette. These data were compiled by Dr. David Blackwell of Southern Methodist University. Global heat flow data (1993) were compiled by Dr. Henry Pollack of the University of Michigan. Data were collected through the World Heat Flow Committee of the International Council of Scientific Unions. These are available on-line.

This data is a series of heat maps to analyze the solar resource available in the state of Sonora. Each figure has 3 heat maps, solar radiation, maximum and minimum temperatures for all the municipalities in the state. This allows to value the photovoltaic potential in the region and analyze the advantages and disadvantages of future solar projects for urban areas in the state. This data are part of the paper : Estimation of Solar Resource Based on Meteorological and Geographical Data: Sonora State in North-Western Territory of Mexico as Case of Study.

The Heat Atlas of the Netherlands is a digital, geographical map on which heat supply and demand in our country are indicated. On the supply side, this concerns (potentially) suitable locations for heat and cold storage (TES), deep geothermal energy, biomass and residual heat. These layers show the potential for ATES systems per neighborhood and per municipality.

The Heat Atlas of the Netherlands is a digital, geographical map on which heat supply and demand in our country are indicated. On the supply side, this concerns (potentially) suitable locations for heat and cold storage (TES), deep geothermal energy, biomass and residual heat. This layer shows the location of industry, their energy demand and CO2 emissions for the purpose of estimating the potential of using residual heat.

Thermal Springs - selected where LOCATION_NAME is like 'THERMAL %'. Geographic Extent: All of the Waikato Region. Positional accuracy is mostly to scales of 1:50000 but some locations have been ‘fixed’ using techniques of greater accuracy. The LOCATED Application holds metadata about the positional accuracy of bore and well locations. Ongoing data collection. Layer updated daily. Geographical location map references are accurate to  50 m (1:50000 scale) unless otherwise indicated by the LOCATED Application. Data Form: GIS Maps, LOCATED Application Reports Digital Format: Oracle Database – LOCATED Application and GIS Layer stored in SQL Server.For further metadata please see feature ENVIRONMENTAL_MONITORING.sdeadmin.LOCATED_THERMAL_SPRINGS in dataset LOCATED - Thermal Springs GIS Layer

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

No Publication Abstract is Available

A series of London-wide climate risk maps has been produced to analyse climate exposure and vulnerability across Greater London. These maps were produced by Bloomberg Associates in collaboration with the Greater London Authority to help the GLA and other London-based organisations deliver equitable responses to the impacts of climate change and target resources to support communities at highest risk. Climate vulnerability relates to people’s exposure to climate impacts like flooding or heatwaves, but also to personal and social factors that affect their ability to cope with and respond to extreme events. High climate risk coincides with areas of income and health inequalities. A series of citywide maps overlays key metrics to identify areas within London that are most exposed to climate impacts with high concentrations of vulnerable populations. In 2022, Bloomberg Associates updated London’s climate risk maps to include additional data layers at a finer geographic scale (LSOA boundaries). These maps were built upon earlier maps using the Transport for London (Tfl) hexagonal grid (often referred to in this report as the “Hex Grid”). In addition, the map interface was updated to allow users to compare individual data layers to the Overall, Heat and Flooding Climate Risk maps. Users can now also see the specific metrics for each individual LSOA to understand which factors are driving risk throughout the city. In 2024, Bloomberg Associates further modernized the climate risk maps by updating the social factor layers to employ more recent (2021) census data. In addition, air temperature at the surface was used in place of just surface temperature, as a more accurate assessment of felt heat. The Mayor is addressing these climate risks and inequalities through the work of the London Recovery Board, which includes projects and programmes to address climate risks and ensure a green recovery from the pandemic. Ambitious policies in the London Environment Strategy and recently published new London Plan are also addressing London’s climate risks. The data layers at the LSOA level are available here to use in GIS software: Climate risk scores (overall, heat, and flood): https://cityhall.maps.arcgis.com/home/item.html?id=22484ef240624e149735ca1aaa4c9ade# Social and physical risk variables: https://cityhall.maps.arcgis.com/home/item.html?id=bc06d80731f146b393f8631a0f98c213#

The Warmteatlas Nederland is a digital, geographical map on which heat supply and demand are indicated in our country. On the supply side, these are (potentially) suitable locations of heat and cold storage (WKO), deep geothermal energy, biomass and residual heat. These layers show the potential for biomass per municipality.

This dataset contains information on vessel movements in the North Sea and Dutch Inland Waterways for the months January, April, July, and October in 2019. It provides a heatmap representation of vessel traffic density during these specific months, which can be useful for various maritime and environmental analyses.

1. File Formats

The dataset is provided in the following file formats:

NetCDF : The primary data files are available in netcdf format. For each grid cell the variables sog (Speed Over Ground) and count (Number of AIS messages) are available

GeoTIFF (Georeferenced Tagged Image File Format): Heatmap images are provided in GeoTIFF format, suitable for geographic visualization.

The dataset is split into tiles. Each tile conforms to the OSM tiling naming scheme.

1. Variables

The dataset includes the following key variables:

Speed Over Ground (SOG): The average vessel's speed over the ground for all the messages.

Count: The number of AIS messages received in this location

1. Data Collection Method

The AIS data used in this dataset was collected from AIS transponders on vessels operating in the North Sea and Dutch Inland Waterways. These transponders transmit information such as vessel position, speed, and identification. The dataset aggregates this information to create heatmap images for analysis. We did this on all the messages. Some ships emit more messages than others. Ships emit messages at higher frequency when sailing than when stationary.

1. Source of Original Data

The original AIS data used to create this dataset was sourced from the AIS archive from Rijkswaterstaat. This dataset was analysed for the purpose of a storymap.

The Heat Atlas of the Netherlands is a digital, geographical map on which heat supply and demand in our country are indicated. On the supply side, this concerns (potentially) suitable locations for heat and cold storage (TES), deep geothermal energy, biomass and residual heat. These layers show the potential for biomass per municipality.

The Heat Atlas of the Netherlands is a digital, geographical map on which heat supply and demand in our country are indicated. On the supply side, this concerns (potentially) suitable locations for heat and cold storage (TES), deep geothermal energy, biomass and residual heat. These layers show the potential for ATES systems per neighborhood and per municipality.