These data describe the planning procedures in their latest known state, specifying their situation in terms of progress and effectiveness. An urban planning procedure lasts on average three years.This description is voluntarily limited to meet a specific objective: show, through summary maps, the geographical distribution and progress of PLU procedures relevant to the management of urban and rural planning policies. These include planning procedures in preparation, revision or repeal. In order to allow an exhaustive summary of the progress of the procedures, the procedures of the past years which have led to urban planning documents which are now enforceable are kept in these data (a planning document is associated with them in the file N_DOCUMENT_URBA_ddd). On the other hand, old urban planning procedures (i.e. those that have resulted in planning documents that are no longer enforceable) and procedures cancelled before their completion are not kept in these data.

The ScaleMaster diagram of Brewer and Buttenfield, "where the scaleLine replaces the timeLine", is a formal tool (Excel sheets) designed to formalize the rules for manual map design and "emphasize changes to the map display" . Inspired by Brewer and Buttenfield, we use ScaleMaster to standardize and formalize changes while zooming and exploring each of pan-scalar map (OSM,Google Maps,Scan IGN). In our methodology, however, we go a step further. The timeline of exploration is also examined in addition to the scaleline of zooming. We focus on map design practices that account for pan-scalar map exploration. For example, we account for generalization changes between scales based on empirically or theoretically justifiable reasons.

we use ScaleMaster to analyze particular and common geographic entities in the maps (including rivers, urban areas, bus stations, and administrative borders) representing but a fraction of all map ontologies (e.g., water, roads, transportation networks, relief, points-of-interest, vegetation, administrative districts). We constructed a ScaleMaster for each of the three pan-scalar maps (OSM, Google Map, Scan IGN).

Our hope is that this first analysis, and the resulting categories below, will lead to critique, comment, and iterative improvement in the future. In other words, our initial findings are just that – outcomes that further exploration on pan-scalar maps can add to, revise, and improve upon.

These data describe the planning procedures in their latest known state, specifying their situation in terms of progress and effectiveness. An urban planning procedure lasts on average three years.This description is voluntarily limited to meet a specific objective: show, through summary maps, the geographical distribution and progress of PLU procedures relevant to the management of urban and rural planning policies. These include planning procedures in preparation, revision or repeal. In order to allow an exhaustive summary of the progress of the procedures, the procedures of the past years which have led to urban planning documents which are now enforceable are kept in these data (a planning document is associated with them in the file N_DOCUMENT_URBA_ddd). On the other hand, old urban planning procedures (i.e. those that have resulted in planning documents that are no longer enforceable) and procedures cancelled before their completion are not kept in these data.

Zoning table consisting of the municipal boundaries in which an administrative procedure prepares or revises an urban planning document These data describe the planning procedures in their latest known state, specifying their situation in terms of progress and effectiveness. On average, a planning procedure lasts three years. This description is voluntarily limited to achieve a specific objective: show, through summary maps, the geographical distribution and progress of PLU procedures relevant to the management of urban and rural planning policies. These include planning procedures in preparation, revision or repeal. In order to allow an exhaustive summary of the progress of the procedures, the procedures of the past years which have led to urban planning documents which are now enforceable are kept in these data (a planning document is associated with them in the file N_DOCUMENT_URBA_ddd). On the other hand, old urban planning procedures (i.e. those that have resulted in planning documents that are no longer enforceable) and procedures cancelled before their completion are not kept in these data.

Most of the text in this description originally appeared on the Mapping Inequality Website. Robert K. Nelson, LaDale Winling, Richard Marciano, Nathan Connolly, et al., “Mapping Inequality,” American Panorama, ed. Robert K. Nelson and Edward L. Ayers, "HOLC staff members, using data and evaluations organized by local real estate professionals--lenders, developers, and real estate appraisers--in each city, assigned grades to residential neighborhoods that reflected their "mortgage security" that would then be visualized on color-coded maps. Neighborhoods receiving the highest grade of "A"--colored green on the maps--were deemed minimal risks for banks and other mortgage lenders when they were determining who should received loans and which areas in the city were safe investments. Those receiving the lowest grade of "D," colored red, were considered "hazardous." Conservative, responsible lenders, in HOLC judgment, would "refuse to make loans in these areas [or] only on a conservative basis." HOLC created area descriptions to help to organize the data they used to assign the grades. Among that information was the neighborhood's quality of housing, the recent history of sale and rent values, and, crucially, the racial and ethnic identity and class of residents that served as the basis of the neighborhood's grade. These maps and their accompanying documentation helped set the rules for nearly a century of real estate practice. " HOLC agents grading cities through this program largely "adopted a consistently white, elite standpoint or perspective. HOLC assumed and insisted that the residency of African Americans and immigrants, as well as working-class whites, compromised the values of homes and the security of mortgages. In this they followed the guidelines set forth by Frederick Babcock, the central figure in early twentieth-century real estate appraisal standards, in his Underwriting Manual: "The infiltration of inharmonious racial groups ... tend to lower the levels of land values and to lessen the desirability of residential areas." These grades were a tool for redlining: making it difficult or impossible for people in certain areas to access mortgage financing and thus become homeowners. Redlining directed both public and private capital to native-born white families and away from African American and immigrant families. As homeownership was arguably the most significant means of intergenerational wealth building in the United States in the twentieth century, these redlining practices from eight decades ago had long-term effects in creating wealth inequalities that we still see today. Mapping Inequality, we hope, will allow and encourage you to grapple with this history of government policies contributing to inequality." Data was copied from the Mapping Inequality Website for communities in Western Pennsylvania where data was available. These communities include Altoona, Erie, Johnstown, Pittsburgh, and New Castle. Data included original and georectified images, scans of the neighborhood descriptions, and digital map layers. Data here was downloaded on June 9, 2020.

The MaineDOT Roadway Context Classification information will be used to adapt transportation decisions,. Using this context information along with roadway function will determine which road user needs should be prioritized. Context data will be used in MaineDOT's new speed limit setting procedure as well as in a new project scoping process connected to the Complete Streets Policy.The map is split into 5 context classifications which make up MaineDOT's classification system. A color key is described below:Red - UrbanOrange - SuburbanDark Blue - VillageGreen - Rural TownWhite - RuralThere are actually have many different criteria that can lead to classification. This detail was necessary to refine the map. You can check on and off the different criteria on the left to see how it impacts the map if you would like.These context classifications will be explained further on the MaineDOT website. This map was created using statewide automated data processing, an automated smoothing process, then corrected through manual review.The data used to make this map includes:Building Density (Area-wide and Close to the Road)Building Area Density (Area-wide and Close to the Road)Intersection Density (Area-wide)Segment Density (Area-wide)Driveway Density (Individual Street)State Urban Compact AreasMPO Planning AreasFor more information about this roadway context data, contact MaineDOT Planning.

These data describe the planning procedures in their latest known state, specifying their situation in terms of progress and effectiveness. An urban planning procedure lasts on average three years.This description is voluntarily limited to meet a specific objective: show, through summary maps, the geographical distribution and progress of PLU procedures relevant to the management of urban and rural planning policies. These include planning procedures in preparation, revision or repeal. In order to allow an exhaustive summary of the progress of the procedures, the procedures of the past years which have led to urban planning documents which are now enforceable are kept in these data (a planning document is associated with them in the file N_DOCUMENT_URBA_ddd). On the other hand, old urban planning procedures (i.e. those that have resulted in planning documents that are no longer enforceable) and procedures cancelled before their completion are not kept in these data.

These data describe the planning procedures in their latest known state, specifying their situation in terms of progress and effectiveness. An urban planning procedure lasts on average three years.This description is voluntarily limited to meet a specific objective: show, through summary maps, the geographical distribution and progress of PLU procedures relevant to the management of urban and rural planning policies. These include planning procedures in preparation, revision or repeal. In order to allow an exhaustive summary of the progress of the procedures, the procedures of the past years which have led to urban planning documents which are now enforceable are kept in these data (a planning document is associated with them in the file N_DOCUMENT_URBA_ddd). On the other hand, old urban planning procedures (i.e. those that have resulted in planning documents that are no longer enforceable) and procedures cancelled before their completion are not kept in these data.

The Charlotte Streets Map is a citywide mobility policy map that categorizes Charlotte's arterial street network into defined street types that reflect our multimodal vision for our streets. Each street type guides public and private investment to plan for and protect envisioned future streets that accommodate our multimodal needs (pedestrian, bike, transit, and car). The Streets Manual was adopted in August 2022, in coordination with the UDO.

These data describe the planning procedures in their latest known state, specifying their situation in terms of progress and effectiveness. On average, a planning procedure lasts three years. This description is voluntarily limited to achieve a specific objective: show, through summary maps, the geographical distribution and progress of PLU procedures relevant to the management of urban and rural planning policies. These include planning procedures in preparation, revision or repeal. In order to allow an exhaustive summary of the progress of the procedures, the procedures of the past years which have led to urban planning documents which are now enforceable are kept in these data (a planning document is associated with them in the file N_DOCUMENT_URBA_ddd). On the other hand, old urban planning procedures (i.e. those that have resulted in planning documents that are no longer enforceable) and procedures cancelled before their completion are not kept in these data.

PLEASE NOTE: This data product is not available in Shapefile format or KML at https://naturalengland-defra.opendata.arcgis.com/datasets/Defra::living-england-habitat-map-phase-4/about, as the data exceeds the limits of these formats. Please select an alternative download format.This data product is also available for download in multiple formats via the Defra Data Services Platform at https://environment.data.gov.uk/explore/4aa716ce-f6af-454c-8ba2-833ebc1bde96?download=true.The Living England project, led by Natural England, is a multi-year programme delivering a satellite-derived national habitat layer in support of the Environmental Land Management (ELM) System and the Natural Capital and Ecosystem Assessment (NCEA) Pilot. The project uses a machine learning approach to image classification, developed under the Defra Living Maps project (SD1705 – Kilcoyne et al., 2017). The method first clusters homogeneous areas of habitat into segments, then assigns each segment to a defined list of habitat classes using Random Forest (a machine learning algorithm). The habitat probability map displays modelled likely broad habitat classifications, trained on field surveys and earth observation data from 2021 as well as historic data layers. This map is an output from Phase IV of the Living England project, with future work in Phase V (2022-23) intending to standardise the methodology and Phase VI (2023-24) to implement the agreed standardised methods.The Living England habitat probability map will provide high-accuracy, spatially consistent data for a range of Defra policy delivery needs (e.g. 25YEP indicators and Environment Bill target reporting Natural capital accounting, Nature Strategy, ELM) as well as external users. As a probability map, it allows the extrapolation of data to areas that we do not have data. These data will also support better local and national decision making, policy development and evaluation, especially in areas where other forms of evidence are unavailable. Process Description: A number of data layers are used to inform the model to provide a habitat probability map of England. The main sources layers are Sentinel-2 and Sentinel-1 satellite data from the ESA Copericus programme. Additional datasets were incorporated into the model (as detailed below) to aid the segmentation and classification of specific habitat classes. Datasets used:Agri-Environment Higher Level Stewardship (HLS) Monitoring, British Geological Survey Bedrock Mapping 1:50k, Coastal Dune Geomatics Mapping Ground Truthing, Crop Map of England (RPA), Dark Peak Bog State Survey, Desktop Validation and Manual Points, EA Integrated Height Model 10m, EA Saltmarsh Zonation and Extent, Field Unit NEFU, Living England Collector App NEFU/EES, Long Term Monitoring Network (LTMN), Lowland Heathland Survey, National Forest Inventory (NFI), National Grassland Survey, National Plant Monitoring Scheme, NEFU Surveys, Northumberland Border Mires, OS Vector Map District , Priority Habitats Inventory (PHI) B Button, European Space Agency (ESA) Sentinel-1 and Sentinel-2 , Space2 Eye Lens: Ainsdale NNR, Space2 Eye Lens: State of the Bog Bowland Survey, Space2 Eye Lens: State of the Bog Dark Peak Condition Survey, Space2 Eye Lens: State of the Bog (MMU) Mountain Hare Habitat Survey Dark Peak, Uplands Inventory, West Pennines Designation NVC Survey, Wetland Inventories, WorldClim - Global Climate DataFull metadata can be viewed on data.gov.uk.

The Living England project, led by Natural England, is a multi-year programme delivering a satellite-derived national habitat layer in support of the Environmental Land Management (ELM) System and the Natural Capital and Ecosystem Assessment (NCEA) Pilot. The project uses a machine learning approach to image classification, developed under the Defra Living Maps project (SD1705 – Kilcoyne et al., 2017). The method first clusters homogeneous areas of habitat into segments, then assigns each segment to a defined list of habitat classes using Random Forest (a machine learning algorithm). The habitat probability map displays modelled likely broad habitat classifications, trained on field surveys and earth observation data from 2021 as well as historic data layers. This map is an output from Phase IV of the Living England project, with future work in Phase V (2022-23) intending to standardise the methodology and Phase VI (2023-24) to implement the agreed standardised methods.

The Living England habitat probability map will provide high-accuracy, spatially consistent data for a range of Defra policy delivery needs (e.g. 25YEP indicators and Environment Bill target reporting Natural capital accounting, Nature Strategy, ELM) as well as external users. As a probability map, it allows the extrapolation of data to areas that we do not have data. These data will also support better local and national decision making, policy development and evaluation, especially in areas where other forms of evidence are unavailable.

Process Description: A number of data layers are used to inform the model to provide a habitat probability map of England. The main sources layers are Sentinel-2 and Sentinel-1 satellite data from the ESA Copericus programme. Additional datasets were incorporated into the model (as detailed below) to aid the segmentation and classification of specific habitat classes.

Datasets used: Agri-Environment Higher Level Stewardship (HLS) Monitoring, British Geological Survey Bedrock Mapping 1:50k, Coastal Dune Geomatics Mapping Ground Truthing, Crop Map of England (RPA), Dark Peak Bog State Survey, Desktop Validation and Manual Points, EA Integrated Height Model 10m, EA Saltmarsh Zonation and Extent, Field Unit NEFU, Living England Collector App NEFU/EES, Long Term Monitoring Network (LTMN), Lowland Heathland Survey, National Forest Inventory (NFI), National Grassland Survey, National Plant Monitoring Scheme, NEFU Surveys, Northumberland Border Mires, OS Vector Map District , Priority Habitats Inventory (PHI) B Button, European Space Agency (ESA) Sentinel-1 and Sentinel-2 , Space2 Eye Lens: Ainsdale NNR, Space2 Eye Lens: State of the Bog Bowland Survey, Space2 Eye Lens: State of the Bog Dark Peak Condition Survey, Space2 Eye Lens: State of the Bog (MMU) Mountain Hare Habitat Survey Dark Peak, Uplands Inventory, West Pennines Designation NVC Survey, Wetland Inventories, WorldClim - Global Climate Data

To better map the urban class and understand how urban lands change over time, we removed rural roads and small patches of rural development from the NLCD developed class and created four wall-to-wall maps (1992, 2001, 2006, and 2011) of urban land. Removing rural roads from the NLCD developed class involved a multi-step filtering process, data fusion using geospatial road and developed land data, and manual editing. Reference data classified as urban or not urban from a stratified random sample was used to assess the accuracy of the 2001 and 2006 urban and NLCD maps. The newly created urban maps had higher overall accuracy (98.7%) than the NLCD maps (96.2%). More importantly, the urban maps resulted in lower commission error of the urban class (23% versus 57% for the NLCD in 2006) with the trade-off of slightly inflated omission error (20% for the urban map, 16% for NLCD in 2006). The removal of approximately 230,000 km2 of rural roads from the NLCD developed class resulted in maps that better characterize the urban footprint. These urban maps are more suited to modeling applications and policy decisions that rely on quantitative and spatially explicit information regarding urban lands. Digital maps of urban land in the United States for 1992, 2001, 2006, and 2011 are available (at a 30-m pixel resolution) as four compressed 2-bit IMG files. The map year is reflected in the file name.

The Visual Resource Inventory Inventories Polygon is a component of the Visual Resource Inventory (VRI) and includes information needed for inventorying for visual values on BLM-managed public lands according to policy direction found in Manual 8400 and related Handbook 8410-1. This VRI inventories feature class contains the following components: VRI Sensitivity Level Rating Unit Polygons, VRI Scenic Quality Rating Unit Polygons, VRI Visual Distance Zone Polygons. This dataset is a subset of the official national dataset, containing features and attributes intended for public release and has been optimized for online map service performance. The Schema Workbook represents the official national dataset from which this dataset was derived.

City of Menlo Park 2023 Calls for ServicePlease view the Menlo Park Police Department Policy Manual for further information.Disclaimer: These data sets may be based upon preliminary information supplied to the Police Department by the reporting parties that have not been verified. In order to protect the privacy of crime victims, addresses are shown at the block level only and specific locations are not identified or have been redacted. Data sets related to sexual offenses, domestic violence, and some crimes involving juveniles have been redacted. The preliminary crime classifications may be changed at a later date based upon additional investigation and there is always the possibility of mechanical or human error. Therefore, the Menlo Park Police Department does not guarantee (either expressed or implied) the accuracy, completeness, timeliness, or correct sequencing of the information and the information should not be used for comparison purposes over time. The Menlo Park Police Department will not be responsible for any error or omission, or for the use of, or the results obtained from the use of this information.

The SFMTA Board of Directors provides policy oversight for the safe and efficient transportation of goods and people in San Francisco in accordance with the San Francisco Charter and the Transit First Policy. This includes the San Francisco Municipal Railway (Muni), automobiles and trucks, taxis, bicycling and walking. The SFMTA Board of Directors also serves as members of the San Francisco Parking Authority.

The Board votes on various items that are captured in the official minutes of each meeting. The map represents the individual items voted on at each board meeting that could be associated with a location either via a block range or intersection. Data are extracted voluntarily by a third-party XTreet (https://xtreet.org) from the PDF minutes. As such, there may be errors or omissions. Also note that data lags minutes as they are transcribed in bulk through a combination of automated and manual processing. The data is provided as is. If you are in need of the accurate accounting of the meetings, please refer to the minutes available at https://www.sfmta.com/about-sfmta/organization/divisions-and-units/board-directors

This highly specialized publication (Ontario Tree Seed Transfer Policy data) is available in English only in accordance with Regulation 671/92, which exempts it from translation under the French Language Services Act. To obtain information in French, please contact the Ministry of Natural Resources and Forestry at (1-800-667-1940). The Ontario Tree Seed Transfer Policy ensures that seed used to regenerate forests has a good chance of producing trees that are adapted to their growing environment. It specifies where seed can be collected and used and the conditions under which seed may be transferred. The data is provided as part of Appendix 1 of the Ontario Tree Seed Transfer Policy. It is available in both table and map formats , and also includes CSV and shape files. Tabular display This dataset includes three tables that show the spatial direction of the seed transfer policy based on the climate similarity analysis (refer to Appendix 1 of the policy for information on the climate similarity analysis): * Table 1. For transitional period: Acceptable seed transfer from the 2010 Seed Zones of Ontario to current seed zones * Table 2. Acceptable seed transfer from the 2010 Seed Zones of Ontario to ecodistricts * Table 3. Acceptable seed transfer among ecodistricts Within the tables, you can click and sort by your location of interest to understand the best seed sources to collect from or deploy to. You can sort by either seed zone or ecodistrict. The policy recommends a climate similarity of 0.9 or greater to the targeted collection or deployment site. Visual display The climate similarity analysis used in developing this policy is also available as an interactive map. Maps are available to help you make seed collection and deployment decisions, including: * collecting seed by ecodistrict or county * deploying seed by ecodistrict * deploying seed by seed zone You can also view: * a detailed map of management unit by seed zone or by ecodistrict * maps to help you make seed transfer decisions related to growing season, precipitation and temperature

PLEASE NOTE: This data product is not available in Shapefile format or KML at https://naturalengland-defra.opendata.arcgis.com/datasets/Defra::living-england-habitat-map-phase-4/about, as the data exceeds the limits of these formats. Please select an alternative download format.This data product is also available for download in multiple formats via the Defra Data Services Platform at https://environment.data.gov.uk/explore/4aa716ce-f6af-454c-8ba2-833ebc1bde96?download=true.The Living England project, led by Natural England, is a multi-year programme delivering a satellite-derived national habitat layer in support of the Environmental Land Management (ELM) System and the Natural Capital and Ecosystem Assessment (NCEA) Pilot. The project uses a machine learning approach to image classification, developed under the Defra Living Maps project (SD1705 – Kilcoyne et al., 2017). The method first clusters homogeneous areas of habitat into segments, then assigns each segment to a defined list of habitat classes using Random Forest (a machine learning algorithm). The habitat probability map displays modelled likely broad habitat classifications, trained on field surveys and earth observation data from 2021 as well as historic data layers. This map is an output from Phase IV of the Living England project, with future work in Phase V (2022-23) intending to standardise the methodology and Phase VI (2023-24) to implement the agreed standardised methods.The Living England habitat probability map will provide high-accuracy, spatially consistent data for a range of Defra policy delivery needs (e.g. 25YEP indicators and Environment Bill target reporting Natural capital accounting, Nature Strategy, ELM) as well as external users. As a probability map, it allows the extrapolation of data to areas that we do not have data. These data will also support better local and national decision making, policy development and evaluation, especially in areas where other forms of evidence are unavailable. Process Description: A number of data layers are used to inform the model to provide a habitat probability map of England. The main sources layers are Sentinel-2 and Sentinel-1 satellite data from the ESA Copericus programme. Additional datasets were incorporated into the model (as detailed below) to aid the segmentation and classification of specific habitat classes. Datasets used:Agri-Environment Higher Level Stewardship (HLS) Monitoring, British Geological Survey Bedrock Mapping 1:50k, Coastal Dune Geomatics Mapping Ground Truthing, Crop Map of England (RPA), Dark Peak Bog State Survey, Desktop Validation and Manual Points, EA Integrated Height Model 10m, EA Saltmarsh Zonation and Extent, Field Unit NEFU, Living England Collector App NEFU/EES, Long Term Monitoring Network (LTMN), Lowland Heathland Survey, National Forest Inventory (NFI), National Grassland Survey, National Plant Monitoring Scheme, NEFU Surveys, Northumberland Border Mires, OS Vector Map District , Priority Habitats Inventory (PHI) B Button, European Space Agency (ESA) Sentinel-1 and Sentinel-2 , Space2 Eye Lens: Ainsdale NNR, Space2 Eye Lens: State of the Bog Bowland Survey, Space2 Eye Lens: State of the Bog Dark Peak Condition Survey, Space2 Eye Lens: State of the Bog (MMU) Mountain Hare Habitat Survey Dark Peak, Uplands Inventory, West Pennines Designation NVC Survey, Wetland Inventories, WorldClim - Global Climate DataFull metadata can be viewed on data.gov.uk.

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

This spatial data contains Surface Management Agency (SMA, also sometimes called Land Status) information for Idaho from the Idaho Bureau of Land Management (BLM). For federal government lands, this data displays the managing agency of the surface of the land, which does not mean the agency "owns" the land. SMA is sometimes referred to as "ownership", although this term is inaccurate when describing public lands. This Surface Management Agency data should not be used to depict boundaries (for example National Forest, National Park, National Wildlife Refuge, or Indian Reservation boundaries among others). Attribute information for the federal and private lands are from the BLM Master Title Plats (MTPs), the BLM case files, the BLM Legacy Rehost 2000 (LR2000) database, and corresponding federal Orders and official documents. Please note that because these official sources are strictly used, OTHER NON-BLM FEDERAL AGENCY LANDS MAY NOT BE ATTRIBUTED CORRECTLY unless the proper documents have been filed with the BLM and the land actions have been noted on the MTPs and in LR2000. Starting in the spring of 2011 a field called AGNCY_NAME is present in the data. The AGNCY_NAME field is intended to indicate the managing agency for polygons coded as OTHER in the MGMT_AGNCY field. The AGNCY_NAME field will not be used for the 100K Map Series published by the BLM for use by the public as all agencies in this field are not included in H-1553 Publication Standards Manual Handbook and, therefore, have no BLM Cartographic Standard. Except for polygons coded as OTHER in the MGMT_AGNCY field, all managing agency information in the AGNCY_NAME field should be the same as that of the MGMT_AGNCY field. The only intended difference between the AGNCY_NAME field and the MGMT_AGNCY field is where the MGMT_AGNCY is OTHER. In this case, the AGNCY_NAME will contain an abbreviation for an agency that is not represented in the H-1553 Publication Standards Manual Handbook. Examples of the agencies there are BIA (Bureau of Indian Affairs), USGS (United States Geological Survey), and FAA (Federal Aviation Administration). Attribute information for the State lands is received primarily through cooperation with the Idaho Department of Lands. This information might not reflect all State agency lands completely. A detailed analysis of State owned lands has not been done since June 2011; therefore, recent changes in ownership of State lands may not be reflected. Inclusion of State land information into this dataset is supplemental and should not be viewed as the authoritative source of State lands; please contact State agencies for questions about State lands. This data does not depict land management arrangements between government agencies such as Memorandums of Understanding or other similar agreements. When this data was originally generated in the early 2000's, the primary source of the geometry was the BLM Geographic Coordinate Database (GCDB), if it was available. In areas where GCDB was/is unavailable, the spatial features are taken from a variety of sources including the BLM Idaho Resource Base Data collection, BLM Idaho Master Title Plat AutoCad files, US Geological Survey Digital Line Graphs (DLGs), and US Forest Service Cartographic Feature Files (CFFs), among others (see Process Steps). It should be stressed that the geometry of a feature may not be GCDB-based in the first place, the geometry may shift away from GCDB due to a variety of reasons (topology procedures, automated software processes such as projections, etc.), and the GCDB-based features are not necessarily currently being edited to match improved GCDB. Therefore this data should NOT be considered actual GCDB data. For the latest Idaho GCDB spatial data, please contact the BLM Idaho State Office Cadastral Department at 208-373-4000. The BLM in Idaho creates and maintains this spatial data. This dataset is derived by dissolving based on the "MGMT_AGNCY" field from the master SMA GIS dataset (which is edited often) kept by the BLM Idaho State Office. Please get a fresh copy of this data a couple times a year as the SMA data is continually changing. Official actions that affect the managing agency happen often and changes to correct errors are always being made. Nevada SMA data was acquired from the BLM Nevada web site and clipped to the area that is managed by Idaho BLM Boise District. The data steward approved this dataset in October 2023. For more information contact us at blm_id_stateoffice@blm.gov.