This dataset represents the results of a project that compiled available range information for three taxonomic groups representing 211 species (159 birds, 45 mammals, and 5 amphibians) identified as Species of Greatest Conservation Need (SGCN) by the 2015 Alaska Wildlife Action Plan (SWAP) Appendix A (https://www.adfg.alaska.gov/index.cfm?adfg=wildlifediversity.swap) in addition to 2 amphibian species native to Alaska.

The goal of this effort was to create an initial set of statewide heatmaps of SGCN richness. Files include: (1) a set of 21 species richness heat maps depicting the sum of overlapping range maps from multiple SGCNs; (2) shapefiles of species range maps for Alaska’s terrestrial SGCN, with all species ranked (high, moderately high, moderate, low) in terms of relative conservation and management priority based on the Alaska Species Ranking System (ASRS; https://accs.uaa.alaska.edu/wildlife/alaska-species-ranking-system); (3) shapefiles of species in decline for birds and marine mammals (as listed in SWAP Appendix A); and (4) a file that cross-walks each SGCN by species code, common name, and scientific name.

Complete information describing how environmental variables correlated with species richness is provided in the final report (http://data.snap.uaf.edu/data/Base/Other/Species/State_Wildlife_Grant_Final_Report_20Sept24.pdf). Species richness maps were derived from species-specific, 6th-level hydrologic unit (HUC12) occupancy maps developed by the Alaska Gap Analysis Project (https://accscatalog.uaa.alaska.edu/dataset/alaska-gap-analysis-project). Hotspot maps highlight all HUCs containing more than 60% of considered amphibian species or 80% of the maximum number of co-occurring bird or mammal species. Species richness values were derived by summing the number of species with overlapping ranges. A gradient boosting machine algorithm quantified relationships between SGCN hotspots and a set of 24 climatic, topographic, and habitat predictors.

It is important to note that species ranges are modeled and extrapolated from limited data. They may be affected by changes in our understanding of species' ranges, changes in taxonomy, and changes in what we consider to be the best tools and data for creating distribution models using presence-only data, and may overestimate actual ranges. These datasets and any associated maps and other products are intended to provide a landscape-level overview only. It is highly recommended that any use of these datasets be undertaken in conjunction with expert advice from the Alaska Department of Fish and Game (see contact information below).

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.

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

No Publication Abstract is Available

No Publication Abstract is Available

Geothermal well data from Southern Methodist University (SMU, 2021) and the U.S. Geological Survey (Sass et al., 2005) were used to create maps of estimated background conductive heat flow across the greater Great Basin region of the western US. The heat flow maps in this data release were created using a process that sought to remove hydrothermal convective influence from predictions of background conductive heat flow. Heat flow maps were constructed using a custom-developed iterative process using weighted regression, where convectively influenced outliers were de-emphasized by assigning lower weights to measurements that are very different from the estimated local trend (e.g., local convective influence). The weighted regression algorithm is 2D LOESS (locally estimated scatterplot smoothing; Cleveland et al., 1992), which was used for local linear regression, and smoothness was controlled by varying the number of nearby points used for each local interpolation. Three maps are i ...

Determining the technical and economic feasibility of geothermal district energy (district heat and cooling) systems is a two-track process. One is directed toward establishing the thermal and chemical characteristics of the resource and the other to establishing the economic and technical viability of building and operating a district energy system. To date most programs have been directed toward identification and characterization of the resource. However, exploration, confirmation drilling, resource characterization and reservoir engineering are all expensive activities that may or may not be justifiable unless the economics of the proposed use of that resource are extremely favorable. Fortunately, at least in the case of geothermal district energy, determining the technical and economic viability of using the resource can now be readily determined at a fraction of the cost of a detailed resource characterization process.

This map features detectable thermal activity over the Caribbean from MODIS satellites for the last 24 hours. MODIS Global Fires is a product of The University of Maryland's Fire Information for Resource Management System (FIRMS). FIRMS integrates remote sensing and GIS technologies to deliver global MODIS hotspot/fire locations to natural resource managers and other stakeholders around the World. MODIS stands for MODerate Resolution Imaging Spectroradiometer. The MODIS instrument is on board NASA’s Earth Observing System (EOS) Terra (EOS AM) and Aqua (EOS PM) satellites. The orbit of the Terra satellite goes from north to south across the equator in the morning and Aqua passes south to north over the equator in the afternoon resulting in global coverage every 1 to 2 days. The EOS satellites have a ±55 degree scanning pattern and orbit at 705 km with a 2,330 km swath width. It takes approximately 2 – 4 hours after satellite overpass for MODIS Rapid Response to process the data, and for FIRMS to update the website. Occasionally, hardware errors mean that it takes longer the 2-4 hours to process the data. For information on the system status of MODIS Rapid Response, click here. We gather this data from the EOSDIS download site. These shapefiles from NASA are parsed using the Aggregated Live Feeds methodology to take the returned information and serve the data through ArcGIS Server as a map service. For performance reasons these layers do not draw when zoomed out beyond 1:20000000. Attribute Information:Latitude and Longitude: The center point location of the 1km (approx.) pixel flagged as containing one or more fires/hotspots (fire size is not 1km, but variable). See What does a hotspot/fire detection mean on the ground?Brightness: The brightness temperature, measured (in Kelvin) using the MODIS channels 21/22 and channel 31.Scan and Track: The actual spatial resolution of the scanned pixel. Although the algorithm works at 1km resolution, the MODIS pixels get bigger toward the edge of the scan. See What does scan and track mean?Date: Acquisition date of the hotspot/active fire pixel.Time: Time of the overpass of the satellite (in UTC).Satellite: Whether the detection was picked up by the Terra or Aqua satellite.Confidence: The detection confidence is a quality flag of the individual hotspot/active fire pixel.Version: Version refers to the processing collection and source of data. The number before the decimal refers to the collection(e.g. MODIS Collection 5). The number after the decimal indicates the source of Level 1B data; data processed in near-real time byMODIS Rapid Response will have the source code “CollectionNumber.0”. Data sourced from MODAPS (with a 2 month lag) and processed by FIRMS using the standard MOD14/MYD14 Thermal Anomalies algorithm will have a source code “CollectionNumber.x”. For example, data with the version listed as 5.0 is collection 5, processed by MRR, data with the version listed as 5.1 is collection 5 data processed by FIRMS using Level 1B data from MODAPS. See What is the difference between data sourced from MODIS Rapid Response and MODAPS Collection 5?Bright.T31: Channel 31 brightness temperature (in Kelvins) of the hotspot/active fire pixel.FRP: Fire Radiative Power. Depicts the pixel-integrated fire radiative power in MW (MegaWatts). FRP provides information on the measured radiant heat output of detected fires. The amount of radiant heat energy liberated per unit time (the Fire Radiative Power) is thought to be related to the rate at which fuel is being consumed (Wooster et. al. (2005))

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.

Heat Maps Software Market Outlook

The heat maps software market has exhibited remarkable growth dynamics, with its global market size estimated at USD 1.2 billion in 2023 and projected to soar to USD 3.8 billion by 2032, reflecting an impressive compound annual growth rate (CAGR) of 13.2%. This robust expansion is primarily driven by the escalating need for data visualization tools in businesses seeking to enhance their analytical capabilities and derive actionable insights from complex datasets. As organizations increasingly recognize the value of visualizing data patterns to make informed decisions, the demand for heat maps software is poised to expand significantly throughout the forecast period.

One of the key growth factors fueling the demand in the heat maps software market is the rising adoption of business intelligence tools across various industry sectors. Organizations are progressively harnessing the power of heat maps to interpret massive volumes of data quickly and intuitively. These visual tools help businesses identify trends, patterns, and correlations within their datasets, ultimately leading to better strategic decision-making. Additionally, the increased deployment of big data and predictive analytics solutions has amplified the significance of heat maps, as they allow users to interpret complex data at a glance and derive actionable insights. This growing reliance on data-driven strategies is expected to sustain the demand for sophisticated heat map solutions across industries.

Another significant market driver is the growing emphasis on customer experience and engagement. Heat maps have proven invaluable in customer analytics, as they provide insights into customer behavior, preferences, and pain points. By visualizing user interactions on websites and digital platforms, businesses can optimize user experience, streamline navigation, and strategically position content to enhance engagement and conversion rates. This has particularly resonated with e-commerce, retail, and service-oriented sectors, where understanding customer behavior is paramount for maintaining competitive advantage. Consequently, the rising focus on customer-centric strategies is poised to propel the demand for heat maps software in the coming years.

Furthermore, the increasing integration of artificial intelligence and machine learning technologies within heat maps software solutions is anticipated to drive market growth further. These advanced technologies enable more sophisticated data analysis and visualization, providing deeper insights and automating the identification of critical patterns and anomalies in data sets. As AI-driven analytics become more prevalent, heat maps software is expected to evolve, offering even more powerful tools for data interpretation. This integration enhances the accuracy and efficiency of data-driven decision-making processes, making heat maps an indispensable tool for businesses striving to stay ahead in an increasingly data-centric world.

Regionally, the heat maps software market exhibits a diverse landscape, with North America currently holding the largest market share. This dominance is attributed to the widespread adoption of advanced data visualization tools and technologies across industries in the region. Moreover, the presence of major market players and the rapid digitization across sectors contribute to North America's position as a leading market for heat maps software. Meanwhile, the Asia Pacific region is expected to witness significant growth, driven by the increasing adoption of digital transformation initiatives and a burgeoning e-commerce sector. As companies across the region continue to leverage data analytics to enhance their competitiveness, the demand for heat maps solutions is set to rise substantially.

Component Analysis

When analyzing the heat maps software market by component, it is essential to consider the two primary segments: software and services. The software segment accounts for the lion's share of the market, driven by the increasing deployment of digital platforms and the need for advanced data visualization tools. Heat maps software solutions are becoming an integral part of business analytics suites, providing enhanced data interpretation capabilities. The continuous evolution of software technology, with innovations focusing on user interface, customization, and integration capabilities, is expected to sustain the dominance of this segment. Additionally, the increasing demand for cloud-based software solutions that offer flexibility, scalability, and cost-effectiveness is further propelling the growth of this segment.

The solr-heatmap extension for CKAN aimed to provide a visualization of geospatial data stored within CKAN resources using heatmaps generated from Solr's spatial search capabilities. This extension likely allowed users to visually identify areas with a high concentration of data points based on geographical coordinates. This could potentially improve data discovery and provide insights into the distribution of geographically referenced datasets. Key Features (Inferred, based on likely functionality and naming): Heatmap Generation: Creates heatmaps directly from geospatial data stored within CKAN resources, visualizing density of datapoints. Solr Integration: Leverages Apache Solr's spatial search functionality to efficiently aggregate and process location data for heatmap generation. This suggests a dependency on a CKAN setup configured to use Solr for search indexing. Configurable Visualization Parameter: Provides configurable options for adjusting the heatmap appearance, such as color schemes, radius, and intensity, to optimize visualization based on the data. Technical Integration: Given its name, the solr-heatmap extension likely integrated with CKAN by adding a new view or visualization option for resources that contain geospatial data. It probably utilized CKAN's plugin architecture to extend the available viewers, adding a "heatmap" option. This component would then communicate with the Solr index to retrieve aggregated geospatial data and generate a dynamically rendered heatmap. Benefits & Impact (Inferred): While this extension is no longer maintained, implementing it may have significantly enhanced data visualization capabilities of CKAN, giving end-users an intuitive way to explore datasets that contain location information. Providing insights that may not be readily apparent through tabular data display. Important Note: This extension is no longer maintained as mentioned in the README. Future functionality can't be guaranteed.

No Publication Abstract is Available

The global heatmap software market is experiencing robust growth, driven by the increasing adoption of data-driven decision-making across various industries. Businesses are leveraging heatmaps to understand user behavior on websites and applications, optimize user experience (UX), improve conversion rates, and enhance overall digital performance. The market's expansion is fueled by the rising popularity of website analytics, A/B testing, and user experience (UX) optimization strategies. The cloud-based segment dominates the market due to its scalability, accessibility, and cost-effectiveness. Large enterprises represent a significant portion of the market due to their higher budgets and complex website structures requiring sophisticated analytics. However, the on-premises segment continues to hold relevance for organizations with stringent data security and compliance requirements. Competition is intense, with established players like Hotjar and Mouseflow alongside newer entrants continually innovating to offer enhanced features and functionalities. The market is expected to see continued growth, driven by technological advancements in AI-powered analytics and the increasing adoption of heatmap tools across mobile applications. Geographic distribution shows North America and Europe holding significant market share, reflecting the high level of digital maturity and adoption of analytics tools in these regions. However, the Asia-Pacific region exhibits strong growth potential, driven by rapid digital transformation and rising internet penetration. Challenges for market growth include the complexity of implementing and interpreting heatmap data, the need for specialized expertise, and the potential for data privacy concerns. Despite these challenges, the market is anticipated to maintain a healthy compound annual growth rate (CAGR), indicating a promising outlook for businesses offering heatmap software solutions and services. Future growth will be significantly shaped by the integration of heatmaps with other analytics tools and the development of more sophisticated and user-friendly interfaces.

The heat profiles are intended to help municipalities in making their transition vision for heat. With the heat profiles, an initial exploration of the municipality's heat demand is visualized. Based on some properties of the buildings in the municipality, an estimate is made of the expected heat demand and the temperature level of this heat. This provides quick and clear insight into the task of the heat transition in your own municipality. The result of the tool can be seen as a first exploration of solutions for a new, sustainable heat supply.

In the heat profiles tool, homes and other buildings are clustered based on the expected required temperature for heat output in the home in 2050: high temperature (HT; >70°C), low-temperature (LT; <55°C) or medium temperature (MT; 55-70°C). The temperature level depends on the amount of heat needed to keep the house warm in the winter (the degree of insulation) and the heat delivery systems. The so-called ‘temperature clusters’ provide insight into the required future heat profile of the municipality; the temperature levels and the amount of heat per area.

The heat profiles were developed by the WarmteTransitieMakers on behalf of the Province of South Holland. More info: https://geo.zuid-holland.nl/Data/Climate/Heat Profiles/Manual%20heat Profiles%20tool%20-%20version%20juni%20%2720.pdf

This map contains a dynamic traffic map service with capabilities for visualizing traffic speeds relative to free-flow speeds as well as traffic incidents which can be visualized and identified. The traffic data is updated every five minutes. Traffic speeds are displayed as a percentage of free-flow speeds, which is frequently the speed limit or how fast cars tend to travel when unencumbered by other vehicles. The streets are color coded as follows:Green (fast): 85 - 100% of free flow speedsYellow (moderate): 65 - 85%Orange (slow); 45 - 65%Red (stop and go): 0 - 45%Esri's historical, live, and predictive traffic feeds come directly from TomTom (www.tomtom.com). Historical traffic is based on the average of observed speeds over the past year. The live and predictive traffic data is updated every five minutes through traffic feeds. The color coded traffic map layer can be used to represent relative traffic speeds; this is a common type of a map for online services and is used to provide context for routing, navigation and field operations. The traffic map layer contains two sublayers: Traffic and Live Traffic. The Traffic sublayer (shown by default) leverages historical, live and predictive traffic data; while the Live Traffic sublayer is calculated from just the live and predictive traffic data only. A color coded traffic map can be requested for the current time and any time in the future. A map for a future request might be used for planning purposes. The map also includes dynamic traffic incidents showing the location of accidents, construction, closures and other issues that could potentially impact the flow of traffic. Traffic incidents are commonly used to provide context for routing, navigation and field operations. Incidents are not features; they cannot be exported and stored for later use or additional analysis. The service works globally and can be used to visualize traffic speeds and incidents in many countries. Check the service coverage web map to determine availability in your area of interest. In the coverage map, the countries color coded in dark green support visualizing live traffic. The support for traffic incidents can be determined by identifying a country. For detailed information on this service, including a data coverage map, visit the directions and routing documentation and ArcGIS Help.

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.

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..

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.

Shallow Geothermal Energy Potential Map consists of a spatial and borehole data base. The Serial Shallow Geothermal Energy Potential Map at a scale of 1:50,000 will be the beginning of the estimation of shallow geothermal resources in terms of the application of optimal technologies and estimation of Poland's energy resources. In the pilot project, carried out between 2017 and 2022, maps were made in the grid of the Detailed Geological Map of Poland at a scale of 1:50 000 and in the grid of the topographic map at a scale of 1:10 000 for areas of urban agglomerations. The analysis covered areas: • in the scale of 1:10 000: o Warsaw (90 sheets), o Wrocław (48 sheets); • in the scale of 1:50 000: o Jelenia Góra region (1 sheet), o Bielsko-Biała region (3 sheets) o Rabka-Zdrój region (2 sheets) o Krynica-Zdrój region (3 sheets). Currently, the project is carried out at a scale of 1:50 000 and covers the following areas: • Gdańsk region (6 sheets), • Supraśl region (2 sheets), • Mielnik region (2 sheets), • Kazimierz Dolny region (4 sheets), • Jelenia Góra region (Sudety Mountains) (5 sheets). The following maps were made on the basis of the study entitled ‘Instruction for making shallow geothermal energy potential and environmental conditions Maps' : • thermal conductivity maps λ [W/m*K] at depths of 40, 70, 100 and 130 m below ground level; • unit heat output maps qv [W/m] for 1800 h of heat pump operation per year at depths of 40, 70, 100 and 130 m below ground level; • unit heat output maps qv [W/m] for 2100 h of heat pump operation per year at a depth of 40, 70, 100 and 130 m below ground level; • Borehole heat exchangers feasibility map according to environmental conditions. To supplement the shallowe geothermal potential maps, were created maps showing the locations of potential geoenvironmental conflicts, where the drilling of boreholes for ground source heat exchangers (GHE), and thus the installation of ground source heat pumps (GHP), is generally possible, where additional information is required or it is generally not possible. Such maps are helpful for the efficient design of individual GHP installations as well as for the determination, of the extent to which low-temperature geothermal energy can meet the heat demand of a region or urban agglomeration for example by local authorities. The maps are complemented by a nationwide GIS database for shallowe geothermal, which will include geological documentations for the purposes of obtaining geothermal heat, collected in the resources of the Central Geological Archives of Polish Geological Institute. In addition, the effective thermal conductivity leff [W/m*K] in the 0÷100m depth interval was determined for selected boreholes from the Central Hydrogeological Databank (deeper than 100 m). On the base of it point map of shallowe geothermal potential for the area of whole Poland was created. The parameterisation was carried out using the thermal conductivity conversion tables from the PORT PC Guidelines, 2013. In the pilot project, 14 011 boreholes were calculated from the Central Hydrogeological Databank. In the current project, the parameterisation will be carried out on the basis of thermal conductivity measurements (both in the fild and in the laboratory) the thermal conductivity conversion tables from the PORT PC Guidelines, 2021.