About the App This app hosts data from Heat Resilience Solutions for Boston (the Heat Plan). It features maps that include daytime and nighttime air temperature, urban heat island index, and extreme heat duration. About the DataA citywide urban canopy model was developed to produce modeled air temperature maps for the City of Boston Heat Resilience Study in 2021. Sasaki Associates served as the lead consultant working with the City of Boston. The technical methodology for the urban canopy model was produced by Klimaat Consulting & Innovation Inc. A weeklong analysis period during July 18th-24th, 2019 was selected to produce heat characteristics maps for the study (one of the hottest weeks in Boston that year). The data array represents the modelled, average hourly urban meteorological condition at 100 meter spatial resolution. This dataset was processed into urban heat indices and delivered as georeferenced image layers. The data layers have been resampled to 10 meter resolution for visualization purposes. For the detailed methodology of the urban canopy model, visit the Heat Resilience Study project website.

The "Transmission Generation Heat Map" data table provides an indication of the potential opportunities (or constraints) to connect to SP Energy Networks' 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 SP Transmission area.The table gives the following information:Location of projectConnection site of projectMW connectedMW increase/decreaseCumulative total capacityProject status and date effective fromFor additional information on column definitions, please click the Dataset schema link below.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. Please raise any potential issues with the data which you have received via the feedback form available at the Feedback tab above (must be logged in to see this).This heatmap will be updated on a monthly basis using the published data from the ESO's TEC register, the latest ECR and the contracted 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)

Topeka Pedestrian Priority Area Heat Map

London Heat Map --------------- The London Heat Map is a tool designed to help you identify areas of high heat demand, explore opportunities for new and expanding district heat networks and to draw potential heat networks and assess their financial feasibility. The new version of the London Heat Map was created for the Greater London Authority by the Centre for Sustainable Energy (CSE) in July 2019. The London Heat Map is regularly updated with new network data and other datasets. Background datasets such as building heat demand was last updated on 26/06/2023. The London Heatmap is a map-based web application you can use to find and appraise opportunities for decentralised energy (DE) projects in London. The map covers the whole of Greater London, and provides very local information to help you identify and develop DE opportunities, including data such as: * Heat demand values for each building * Locations of potential heat supply sites * Locations of existing and proposed district heating networks * A spatial heat demand density map layer The map also includes a user-friendly visual tool for heat network design. This is intended to support preliminary techno-economic appraisal of potential district heat networks. The London Heat Map is used by a wide variety of people in numerous ways: * London Boroughs can use the new map to help develop their energy master plans. * Property developers can use the map to help them meet the decentralised energy policies in the London Plan. * Energy consultants can use the map to gather initial data to inform feasibility studies. More information is available here, and an interactive map is available here. Building-level estimated annual and peak heat demand data from the London Heat Map has been made available through the data extracts below. The data was last updated on 26/06/2023. The data contains Ordnance Survey mapping and the data is published under Ordnance Survey's 'presumption to publish'. © Crown copyright and database rights 2023. The Decentralised Energy Master planning programme (DEMaP) ---------------------------------------------------------- The Decentralised Energy Master planning programme (DEMaP), was completed in October 2010. It included a heat mapping support package for the London boroughs to enable them to carry out high resolution heat mapping for their area. To date, heat maps have been produced for 29 London boroughs with the remaining four boroughs carrying out their own data collection. All of the data collected through this process is provided below. ### Carbon Calculator Tool Arup have produced a Carbon Calculator Tool to assist projects in their early estimation of the carbon dioxide (CO2) savings which could be realised by a district heating scheme with different sources of heating. The calculator's estimates include the impact of a decarbonising the electrical grid over time, based on projections by the Department for Energy and Climate Change, as well as the Government's Standard Assessment Procedure (SAP). The Excel-based tool can be downloaded below. ### Borough Heat Maps Data and Reports (2012) In March 2012, all London boroughs did a heat mapping exercise. The data from this includes the following and can be downloaded below: * Heat Load for all boroughs * Heat Supplies for all boroughs * Heat Network * LDD 2010 database * Complete GIS London Heat Map Data The heat maps contain real heat consumption data for priority buildings such as hospitals, leisure centres and local authority buildings. As part of this work, each of the boroughs developed implementation plans to help them take the DE opportunities identified to the next stages. The implementation plans include barriers and opportunities, actions to be taken by the council, key dates, personnel responsible. These can be downloaded below. Other Useful Documents ---------------------- Other useful documents can be downloaded from the links below: Energy Masterplanning Manual Opportunities for Decentralised Energy in London - Vision Map London Heat Network Manual London Heat Network Manual II

Area-wide modeled near-surface temperature for 7-8 pm on July 27, 2020, based on temperature and humidity data collected for a one-day heat mapping project conducted by King County, Seattle Public Utilities, and the City of Seattle. Data collected on July 27, 2020 in partnership with project volunteers and CAPA Strategies. Data analysis and maps produced by CAPA strategies. This predictive temperature model was created from multi-band land cover rasters from Sentinel-2 satellite and raw heat data from sensor SD cards using the 70:30 holdout method.Heat maps also available for 6-7 am and 7-8 pm. Results can be viewed using this ArcGIS web app viewer. More information on the project available in Heat Watch Report for Seattle & King County. Contact CAPA Strategies for questions on the data, maps, and data analysis methods.

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).

The ability to interpret the predictions made by quantitative structure–activity relationships (QSARs) offers a number of advantages. While QSARs built using nonlinear modeling approaches, such as the popular Random Forest algorithm, might sometimes be more predictive than those built using linear modeling approaches, their predictions have been perceived as difficult to interpret. However, a growing number of approaches have been proposed for interpreting nonlinear QSAR models in general and Random Forest in particular. In the current work, we compare the performance of Random Forest to those of two widely used linear modeling approaches: linear Support Vector Machines (SVMs) (or Support Vector Regression (SVR)) and partial least-squares (PLS). We compare their performance in terms of their predictivity as well as the chemical interpretability of the predictions using novel scoring schemes for assessing heat map images of substructural contributions. We critically assess different approaches for interpreting Random Forest models as well as for obtaining predictions from the forest. We assess the models on a large number of widely employed public-domain benchmark data sets corresponding to regression and binary classification problems of relevance to hit identification and toxicology. We conclude that Random Forest typically yields comparable or possibly better predictive performance than the linear modeling approaches and that its predictions may also be interpreted in a chemically and biologically meaningful way. In contrast to earlier work looking at interpretation of nonlinear QSAR models, we directly compare two methodologically distinct approaches for interpreting Random Forest models. The approaches for interpreting Random Forest assessed in our article were implemented using open-source programs that we have made available to the community. These programs are the rfFC package (https://r-forge.r-project.org/R/?group_id=1725) for the R statistical programming language and the Python program HeatMapWrapper [https://doi.org/10.5281/zenodo.495163] for heat map generation.

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.

Framing ecological restoration and monitoring goals from a human benefits perspective (i.e., ecosystem services) can help inform restoration planners, surrounding communities, and relevant stakeholders about the direct benefits they may obtain from a specific restoration project. We used a case study of tidal wetland restoration in the Tillamook River watershed in Oregon, USA, to demonstrate how to identify and integrate community stakeholders/beneficiaries and the environmental attributes they use to inform the design of and enhance environmental benefits from ecological restoration. Using the U.S. Environmental Protection Agency’s Final Ecosystem Goods and Services (FEGS) Scoping Tool, we quantify the types of ecosystem services of greatest common value to stakeholders/beneficiaries that lead to desired benefits that contribute to their well-being in the context of planned uses that can be incorporated into the restoration project. This case study identified priority stakeholders, beneficiaries, and environmental attributes of interest to inform restoration goal selection. This novel decision context application of the FEGS Scoping Tool also included an effort focused on how to communicate the connections between stakeholders, and the environmental attributes of greatest interest to them using heat maps.

These are the Small Business Express projects in the Department of Economic and Community Development - Business Assistance Portfolio dataset. This data is updated in accordance with with the schedule of that dataset. This list also includes out of business projects and inactive (repaid/contract no longer active) projects.

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.

This package contains a map surface that depicts the estimated spatial variation of conductive heat flow (mW/m²) in a portion of northern Nevada, the extent of the ‘Nevada Machine Learning Project’ (DE-EE0008762). It was generated using well locations that had an estimated heat flow value from a measured thermal gradient and thermal conductivity, mainly using data from Southern Methodist University, with some additional USGS data. Well data are included along with and a map surface depicting estimated standard error of the heat flow interpolation.

The 'Warmtekaart Vlaanderen 2019' was commissioned by the Flemish Energy and Climate Agency to implement the EU Directive 2012/27/EU on energy efficiency and the Renewable Energy Directive (EU) 2018/2001. The main products are maps for 2019 for the territory of Flanders with the heat demand from large and small consumers, results at the level of the municipalities and the statistical sectors, maps of the existing and planned heat networks and, finally, the locations of potential heat supply points. The study was carried out by VITO in collaboration with the distribution system operator Fluvius. You can consult the accompanying report here: https://www.energiesparen.be/ Warmtekaart. For the year 2019, 58 existing heating networks in Flanders could be identified, or 92 km in total (slot length). 52 of them could be mapped with their route (see map layer 'Existing heat networks 2019 (lines)'. This map layer shows the location of the other 6 heat networks by means of a point. The map layer contains the following information for each heat network: name of the project, type of network, municipality, heat network operator, heat network supplier, supply to residential/industrial/tertiary, temperature level, slot length, generators, amount of energy supplied to heat network, share of (non-)renewable (non-)residual heat, financing received, financing from which resources and finally whether an expansion of the network is planned.The term heating network means the systems that fall under the definition of heating or cooling network in the Energy Decree, Article 1.1.3.,133/2°.For more background information, reference is made to the Heat Map report.

Under the Energy Efficiency Directive and the Renewable Energy Directive, EU Member States were required to report national figures and plans on heat and cold by the end of June 2024. This is in line with the European Energy Union's strategy to achieve carbon neutrality by 2050. The main products are maps for the territory of Flanders with the heat demand at the level of the municipalities and the statistical sectors, maps of the existing and planned heat networks and finally also locations of potential heat supply points. The study was carried out by the Flemish Energy and Climate Agency. You can consult the accompanying report here: https://www.vlaanderen.be/building-living-and-energy/green-energy/heat map. For the year 2024, in addition to the existing heat networks, 19 locations could also be identified where plans are being made to build or expand a heat network. For these planned nets, the map layer also contains information about: name of the project, owner, postcode, municipality and whether or not a subsidy has been applied for under the call for green heat/residual heat. For more background information, please refer to the Heat Map report.

This dataset holds results from an urban heat study conducted in Maitland LGA using data collect from the summer of 2019/20 and Australian Bureau of Statistics 2016 dataset. Maitland City Council undertook this urban heat mapping project to identify priority suburbs within the LGA to inform targeted initiatives to mitigate the heat island effect.Data is aggregated by the ABS to the area of a Statistical Area Level 2 (SA2) for the following variables:Land surface temperatures (LST) for summer 2019Normalised Difference Vegetation Index (NDVI)Heat Vulnerability Scores (5 is most vulnerable)

The Scotland Heat Map provides estimates of annual heat demand for almost 3 million properties in Scotland. Demand is given in kilowatt-hours per year (kWh/yr). Property level estimates can be combined to give values for various geographies. Both domestic and non-domestic properties are included. This dataset gives the total estimated heat demand of properties within each 2020 Settlement in Scotland in kilowatt-hours per year (kWh/yr). Heat demand is calculated by combining data from a number of sources, ensuring that the most appropriate data available is used for each property. The data can be used by local authorities and others to identify or inform opportunities for low carbon heat projects such as district heat networks. The Scotland Heat Map is produced by the Scottish Government. The most recent version is the Scotland Heat Map 2022, which was released to local authorities in November 2023. More information can be found in the documentation available on the Scottish Government website: https://www.gov.scot/publications/scotland-heat-map-documents/

This is a metadata compilation for maps published by Massachusetts Geological Survey as Miscellaneous Map Series M-13-01 through M-13-08 for the Massachusetts Geothermal Energy Project in 2013. The maps inlcude thermal conductivity of bedrock and soil, heat production, inferred heat flow, and temperatures at 3-, 4-, 5-, and 6-Km depths.The metadata compilation is published as an Excel workbook containing header features including title, description, author, citation, originator, distributor, and resource URL links to scanned maps (PDFs) for download. The Excel workbook contains contains six worksheets, including information about the template, notes related to revisions of the template, resource provider information, the data, a field list (data mapping view), and vocabularies (data valid terms) used to populate the data worksheet . The metadata was provided by the Massachusetts Geological Survey and made available for distribution through the National Geothermal Data System.

A publicly-accessible website to measure and visualize similarities and differences between molecular profiles of complex microbial communities. The project includes visualization tools such as heat maps that simultaneously compare the taxonomic distributions of multiple datasets and 3-D charts of the frequency distributions of 16S rRNA tags. Analytical tools include Chao diversity estimates and rarefaction curves. As a service to the community, researchers have the opportunity to upload their own data to the site for private viewing with the full range of data and analysis tools. Public data can be downloaded for further analysis locally.

The radio map, or spectrum environment map (SEM), can visualize the information of invisible electromagnetic spectrum, and is vital for monitoring, management, and security of spectrum resources in cognitive radio (CR) networks. It is useful for the abnormal spectral activity detection, radiation source localization, spectrum resource management, etc. This project presents a measured radio map dataset in the urban scenario with multiple radiation sources, aiming to address the limitation of open datasets for radio map in realistic multi-source dynamic scenarios. We used a spectral signal receiving system to measure the signal intensity of multiple radiation sources in the urban scene. This project includes two datasets as 1) Raw radio map measurement data (30 MHz, 115 MHz, and 2 GHz), in the format of.csv. It includes entries such as longitude, latitude, altitude, start and end frequencies, frequency interval, number of acquisition points, and signal strength. 2) Raw spectrum tensor data (30 MHz, 115 MHz, and 2 GHz), in the format of.mat.

More details about the construction of the spectrum map and dataset can be found in the following references. [1]. Q. Zhu et al., DEMO Abstract: An UAV-based 3D Spectrum Real-time Mapping System, 2022 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), New York, NY, USA, 2022, pp. 1-2. [2] J. Wang et al., "Sparse Bayesian Learning-Based Hierarchical Construction for 3D Radio Environment Maps Incorporating Channel Shadowing," in IEEE Transactions on Wireless Communications, vol. 23, no. 10, pp. 14560-14574, Oct. 2024. [3]. Q. Gao, et al. Time-Variant Radio Map Reconstruction with Optimized Distributed Sensors in Dynamic Spectrum Environments[J]. IEEE Internet of Things Journal, early access, Feb.2025, doi: 10.1109/JIOT.2025.3545542.