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 raster dataset gives the total estimated heat demand of properties within 500m x 500m grid squares covering all of Scotland. 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/

Heat Map/Points showing new Single Family Housing for Years 2012-2016 including: Parcel Number, Category, Issued Date, Valuation, Square Footage, and Year.By using this dataset you acknowledge the following:Kansas Open Records Act StatementThe Kansas Open Records Act provides in K.S.A. 45-230 that "no person shall knowingly sell, give or receive, for the purpose of selling or offering for sale, any property or service to persons listed therein, any list of names and addresses contained in, or derived from public records..." Violation of this law may subject the violator to a civil penalty of $500.00 for each violation. Violators will be reported for prosecution.By accessing this site, the user makes the following certification pursuant to K.S.A. 45-220(c)(2): "The requester does not intend to, and will not: (A) Use any list of names or addresses contained in or derived from the records or information for the purpose of selling or offering for sale any property or service to any person listed or to any person who resides at any address listed; or (B) sell, give or otherwise make available to any person any list of names or addresses contained in or derived from the records or information for the purpose of allowing that person to sell or offer for sale any property or service to any person listed or to any person who resides at any address listed."

The Scotland Heat Map provides estimates of heat demand for all properties in Scotland. To indicate reliability, each estimate is assigned a confidence level from 1 to 5. Level 1 is least reliable and level 5 most reliable. This is mainly determined by the presence of information that would directly impact on heat demand in the estimate's source data. For example, estimates based on data that includes building type, age and floor area would be more reliable than estimates based solely on floor area. This raster dataset gives the average (mean) confidence level of properties within 50m x 50m grid squares covering all of Scotland. The Scotland Heat Map is a tool to help plan for the reduction of carbon emissions from heat in buildings. Average confidence level is an indicator of reliability of the heat demand estimates within an area and allows planners to decide whether they meet their needs. The 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/

The Scotland Heat Map provides the locations of existing and planned heat networks. Both communal and district heat networks are included. Data about each network includes, where available, heat capacity size category, network name, status (either 'operational' or 'in development') and the main technology used (for example, 'boiler'). There is only one point location for each network, the data does not show all connected properties or pipe layouts. Networks can serve domestic properties, non-domestic properties or a mixture of the two. Heat networks have the potential to reduce carbon emissions from heating buildings. Alongside other heat map datasets, information on existing and planned networks is used to identify further opportunities to reduce carbon emissions. For example, by connecting more buildings to an existing network or by replacing the energy source with a nearby lower carbon alternative. Data on heat networks comes from two sources. These are: the UK Department for Energy Security and Net Zero's Heat Networks (Metering and Billing) Regulations (HNMBR) dataset and Zero Waste Scotland's Low Carbon Heat Database (LCHD). The most recent data available is up to end July 2022 for the HNMBR dataset (though the majority of the HNMBR data included in the heat map is up to end December 2018) and January 2022 for the LCHD. More information can be found in the documentation available on the Scottish Government website: https://www.gov.scot/publications/scotland-heat-map-documents/

A three-dimensional (3D) map of the Cooper Basin region has been produced from 3D inversions of Bouguer gravity data using geological data to constrain the inversions. The 3D map delineates regions of low density within the basement of the Cooper/Eromanga Basins that are inferred to be granitic bodies. This 3D data release constitutes the second version of the 3D map of the Cooper Basin region. It builds on Version 1 of the Cooper Basin Region Geological map, released in 2009.

The Cooper Basin region is coincident with a prominent geothermal anomaly and forms part of a broad area of anomalously high heat flow. High-heat-producing granites, including granodiorite of the Big Lake Suite (BLS) at the base of the Cooper and Eromanga Basins sequences combined with thick Cooper/Eromanga sedimentary sequences that provide a thermal blanketing effect, result in temperatures as high as 270° C at depths <5 km. The location and characteristics of other granitic bodies are poorly understood and accurately identifying them is an important first step towards future geothermal exploration in this region.

3D Bouguer gravity field inversion modelling was carried out using the UBC inversion software. An initial gravity inversion was performed using seismic horizons to constrain the 3D distribution of the Cooper/Eromanga Basin sediments. Densities, derived from seismic velocities from a refraction seismic survey in the region, were assigned to the Cooper/Eromanga sediments in order to constrain their gravity contribution. A series of Iso-surfaces were generated, enclosing low density lobes within the basement of the initial sediment-constrained inversion model. Gravity 'worms' were used to pick the iso-surfaces that approximate the lateral sub-sediment extent of potential granites within the basement. A series of subsequent granite-constrained inversions were generated by assigning different maximum cut-off depths to the lobes. The inversion model that produced the most 'neutral' result had a maximum cut-off depth of 10 km.

The 3D map was then used to predict temperatures throughout the volume of the map. Thermal properties were sourced from the literature and from direct measurements. Forward predictions of temperatures were carried out using the Simulator for HEat and MAss Transport (SHEMAT) software package. Thermal properties were iteratively updated until a satisfactory match was achieved between the model and temperature measurements. The resulting temperature distribution gives strongly elevated temperatures over the BLS, as well as broader regions of elevated temperature in the northwest of the study area toward Mt Isa, under the Adavale Basin in the north-east of the study area, and south-east of the BLS.

Uncertainty was analysed using a stochastic modelling technique. A sensitivity analysis was first performed to select the parameters which, when varied, had the greatest effect on the predicted temperatures. These parameters are: thermal conductivity of the basin sediments, heat production of the basement and granite units, and basal heat flux. Stochastic models were then run, giving the standard deviation of the temperature at each point in the model. The resulting standard deviation distribution shows that areas of highest predicted temperature are also areas of highest error. However, when the standard deviation values are converted to percentage error, a different pattern emerges: Highest error values are observed where the Cooper Basin sediments are thickest. Lower error values are observed over the BLS and in the southeast of the model area.

The Properties Vulnerable to Heat Impact report, produced by Arup, maps London's heat risk across homes, neighbourhoods, and essential properties in the wake of climate change.

The study focused on essential settings, emphasising areas where occupants are especially vulnerable to heat-related hazards. This included schools, hospitals, care homes residential properties and neighbourhoods.

Properties Vulnerable to Heat Impact Report | London City Hall

Land Surface Temperature (LST) is a key indicator of land surface states, and can provide information on surface-atmosphere heat and mass fluxes, vegetation water stress, and soil moisture. A daily, day and night, LST data set for continental Africa, including Madagascar, was derived from Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC; 4 km resolution) data for the 6-year lifetime of the NOAA-14 satellite (from 1995 to 2000) using a modified version of the Global Inventory Mapping and Monitoring System (GIMMS) (Tucker et al., 1994). The data were projected into Albers Equal Area and aggregated to 8 km spatial resolution. The data were cloud-filtered with CLAVR-1 algorithm (Stowe et al., 1999). The LST values were estimated with a split-window technique (Ulivieri et al., 1994) that takes advantage of differential absorption of the thermal infrared signal in bands 4 and 5. The emissivity of the surface was generated using a land cover classification map (Hansen et al., 2000) combined with the FAO soil map of Africa (FAO-UNESCO, 1977) and additional maps of tree, herbaceous, and bare soil percent cover (DeFries et al., 2000). Collateral products include cloud mask, time-of-scan, latitude and longitude, and land/water mask files.The data are in flat binary files. Each data file contains 1152 columns and 1152 rows, in signed integer format (2 bytes), with 8 km by 8 km spatial resolution. A unique map exists for each day and each night of the 6-year NOAA-14 lifetime. The data are best used to infer broad temporal and spatial trends rather than pixel-by-pixel values.

Chicago Heights Municipal Atlas Data Layers - March 25, 2020 uploadData LayersWards (2018)* Alderman's name and contact emailCity-Owned Parcels* Unknown year of data, but differs from 2016 Cook County Assessor dataHeritage Preservation Overlay DistrictVoting Precincts (2018)Municipal Boundary (2016)* Differs from Cook County municipal boundary data, but this boundary will be prioritized.Vacant Properties (2016) - Points [for heat map]Vacant Properties (2016) - ParcelsLand Bank Properties (2017)Lawn Maintenance Parcels (2019)