datasets for heatmap, based on rs and div values.Data related to the research regarding the relationship between idr & citation accumulation.

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 Benefits & Impact (Inferred): While this extension is no longer maintained, implementing it may have significantly enhanced data visualization capabilities of CKAN, giving end-users an intuitive way to explore datasets that contain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 Important Note: This extension is no longer maintained as mentioned in the README.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.

The map simulates a chain of coffee stores in Manhattan. The heat map layer helps explain the public's impression that "there's one on every corner."A simple map of store locations is useful to a point. When the map's symbols start to "stack up" on one another, a heat map can often help visualize the local area better than the collective "dots on the map" can.The data in this map was created for demonstration purposes only.

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)

The hurricane heatmap was generated using the NOAA/IBTrACS/v4 dataset, which was filtered to focus on the North Atlantic Basin from January 1950 to October 2024. This dataset, sourced from The International Best Track Archive for Climate Stewardship (IBTrACS), offers detailed information on tropical cyclone locations and intensity, providing critical insight into storm behavior over the decades. The map visually represents the highest concentration of hurricane locations, with the intensity of storm occurrences depicted through point data derived from IBTrACS. The data utilized for this heatmap was exported from the Google Earth Engine JavaScript code editor as a GeoTIFF file, with a resolution of 75 km² per pixel, ensuring a balance between visual clarity and the preservation of spatial details. By leveraging the power of Google Earth Engine, this visualization provides an effective way to analyze and explore the frequency and distribution of hurricanes across the North Atlantic, helping to highlight regions most prone to hurricane activity and offering valuable information for climate research and disaster preparedness.

These are species-level heatmaps of bacterial abundances across all 4 timepoints of the study, and at each sampling location. These are to aid in the visualization of seeing the different microbial populations from stream, to sewage, to treated water.

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 dataset reflects reported incidents of crime (with the exception of murders where data exists for each victim) that occurred in the City of Chicago from 2001 to present, minus the most recent seven days. Data is extracted from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system. In order to protect the privacy of crime victims, addresses are shown at the block level only and specific locations are not identified. Should you have questions about this dataset, you may contact the Research & Development Division of the Chicago Police Department at 312.745.6071 or RandD@chicagopolice.org. Disclaimer: These crimes may be based upon preliminary information supplied to the Police Department by the reporting parties that have not been verified. 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 Chicago 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 Chicago 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. All data visualizations on maps should be considered approximate and attempts to derive specific addresses are strictly prohibited. The Chicago Police Department is not responsible for the content of any off-site pages that are referenced by or that reference this web page other than an official City of Chicago or Chicago Police Department web page. The user specifically acknowledges that the Chicago Police Department is not responsible for any defamatory, offensive, misleading, or illegal conduct of other users, links, or third parties and that the risk of injury from the foregoing rests entirely with the user. The unauthorized use of the words "Chicago Police Department," "Chicago Police," or any colorable imitation of these words or the unauthorized use of the Chicago Police Department logo is unlawful. This web page does not, in any way, authorize such use. Data is updated daily Tuesday through Sunday. The dataset contains more than 65,000 records/rows of data and cannot be viewed in full in Microsoft Excel. Therefore, when downloading the file, select CSV from the Export menu. Open the file in an ASCII text editor, such as Wordpad, to view and search. To access a list of Chicago Police Department - Illinois Uniform Crime Reporting (IUCR) codes, go to http://data.cityofchicago.org/Public-Safety/Chicago-Police-Department-Illinois-Uniform-Crime-R/c7ck-438e

This dataset stores a numpy.array that pixels of a world map of estimated waiting_times for hitchhiking. It contains a second numpy.array that provides uncertainties for these estimations. These values can be used to produce a map like the following:

The splits of the dataset in data are named by the date they were created at and are based on all available data points that are available at this time. New maps (splits) are created regularly by… See the full description on the dataset page: https://huggingface.co/datasets/Hitchwiki/hitchhiking-heatmap.

Visualization of connected vehicle trajectory data along a work zone on Indiana interstate I-69 in northbound direction for 15 miles section from mile marker location 245 to 260 using connected vehicle records on Thursday, May 11, 2023.

It is often reported that the library is overly cold. It is necessary to have a complete understanding of the temperature distribution in various library sections. The library also listens to feedback from LibQUAL+, a library services quality survey. A heatmap will be useful in providing information about the temperature conditions in different locations. With this, users can find and identify areas that are at their optimal temperature for learning. By helping users to find the most comfortable learning environment, the studying experience of users will be improved.

All BPD data on Open Baltimore is preliminary data and subject to change. The information presented through Open Baltimore represents Part I victim based crime data. The data do not represent statistics submitted to the FBI's Uniform Crime Report (UCR); therefore any comparisons are strictly prohibited. For further clarification of UCR data, please visit http://www.fbi.gov/about-us/cjis/ucr/ucr. Please note that this data is preliminary and subject to change. Prior month data is likely to show changes when it is refreshed on a monthly basis. All data is geocoded to the approximate latitude/longitude location of the incident and excludes those records for which an address could not be geocoded. Any attempt to match the approximate location of the incident to an exact address is strictly prohibited.

RADARSAT-1, in operation from 1995 to 2013, is Canada's first earth observation satellite. Developed and operated by the Canadian Space Agency (CSA), it has provided essential information to government, scientists and commercial users. Ultimately, the RADARSAT-1 mission generated the largest synthetic-aperture radar (SAR) data archive in the world. In April 2019, 36,000 images were made accessible through the Earth Observation Data Management System (eodms-sgdot.nrcan-rncan.gc.ca). A heatmap of processed images was produced by the CSA and helps visualize the density of images available by mapped sector during the RADARSAT-1 mission.

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 emergence of urban heat as a climate-induced health stressor is receiving increasing attention among researchers, practitioners, and climate educators. However, the measurement of urban heat poses several challenges with current methods leveraging either ground based, in situ observations, or satellite-derived surface temperatures estimated from land use emissivity. While both techniques contain inherent advantages and biases to predicting temperatures, their integration may offer an opportunity to improve the spatial resolution and global application of urban heat measurements. Using a combination of ground-based measurements, machine learning techniques, and spatial analysis, we addressed three research questions: (1) How much do ambient temperatures vary across time and space in a metropolitan region? (2) To what extent can the integration of ground-based measurements and satellite imagery help to predict temperatures? (3) What landscape features consistently amplify and temper heat? We applied our analysis to the city of Richmond, Virginia, using geocomputational machine learning processes on data collected on days when maximum air temperatures were above the 90th percentile of historic averages. Our results suggest that the urban microclimate was highly variable—with differences of up to 10 C between coolest and warmest locations at the same time—and that these air temperatures were primarily dependent on underlying landscape features. Additionally, we found that integrating satellite data with ground-based measures provided highly accurate and precise descriptions of temperatures in all three study regions. These results suggest that accurately identifying areas of extreme urban heat hazards for any region is possible through integrating ground-based temperature and satellite data.

Dataset content available to registered users only

ENWL Primary Capacity Heatmap

Dataset depicting all Primary Substations represented as interlocking voronoi polygons, The information contained in the dataset will enable developers to assess the level of capacity that might be available for new connections to our network. This capacity dataset contains all the available capacity information on the Primary Substations connected to our network.

The information held in this dataset, along with the three further datasets listed below

Grid Supply Point Connection Queue

Grid Supply Point Capacity

Bulk Supply Point Capacity

Have been combined in a map page

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While we use reasonable endeavours to ensure that the data contained within this dataset is accurate, we do not accept any responsibility or liability for the accuracy or the completeness of the content held, or for any loss which may arise from reliance on this dataset and/or its related information.

If you have any query related to the Primary Capacity Heatmap data, please contact us

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AI-Enhanced Audience Heatmap Analytics Market Outlook

As per our latest research, the AI-Enhanced Audience Heatmap Analytics market size has reached USD 3.1 billion in 2024 globally, reflecting robust adoption across diverse industries. The market is expanding at a CAGR of 17.2% and is projected to attain a value of USD 13.9 billion by 2033. This impressive growth trajectory is driven by the escalating need for real-time audience insights, improved customer engagement strategies, and the integration of advanced AI technologies in analytics platforms.

The primary growth factor propelling the AI-Enhanced Audience Heatmap Analytics market is the increasing demand for actionable consumer insights. Organizations across sectors such as retail, entertainment, and transportation are leveraging AI-powered heatmap analytics to understand crowd movement, dwell times, and engagement patterns in physical and digital environments. This enables businesses to optimize layouts, enhance user experiences, and drive revenue growth. The ability of AI algorithms to process vast datasets in real-time, identify trends, and provide predictive analytics is revolutionizing how companies interpret and act upon audience behavior. Furthermore, the proliferation of IoT devices and sensors has significantly expanded the data pool, allowing for more granular and accurate heatmap analytics.

Another critical driver is the rapid digital transformation witnessed across industries post-pandemic. Enterprises are increasingly deploying AI-Enhanced Audience Heatmap Analytics to support data-driven decision-making and to remain competitive in an evolving marketplace. The retail sector, for instance, is utilizing these solutions to manage store traffic, optimize product placements, and personalize marketing campaigns. Similarly, sports and entertainment venues are adopting audience heatmap analytics to improve crowd management, security protocols, and fan engagement. The integration of AI with advanced visualization tools is enabling stakeholders to derive deeper insights and foster innovation in customer experience management.

The evolution of cloud computing and scalable deployment models is further accelerating market growth. Cloud-based AI-Enhanced Audience Heatmap Analytics solutions are enabling organizations of all sizes to access sophisticated analytics capabilities without the need for significant capital investment in hardware or IT infrastructure. This democratization of analytics is fostering adoption among small and medium enterprises, which are now able to leverage AI-driven insights previously accessible only to large corporations. Additionally, the growing focus on data privacy and compliance is prompting vendors to develop secure, compliant solutions that address regulatory requirements while delivering robust analytics performance.

From a regional perspective, North America currently dominates the AI-Enhanced Audience Heatmap Analytics market, accounting for the largest share in 2024. The region’s advanced technological infrastructure, high adoption of AI-driven solutions, and significant investments in R&D are key contributors to its leadership position. However, the Asia Pacific region is emerging as the fastest-growing market, fueled by rapid urbanization, expanding retail and entertainment sectors, and increasing digitalization initiatives. Europe also presents substantial growth opportunities, particularly in the transportation and healthcare sectors, where audience analytics are being leveraged to enhance operational efficiency and patient experiences.

Component Analysis

The AI-Enhanced Audience Heatmap Analytics market by component is segmented into software, hardware, and services. The software segment constitutes the largest share in 2024, driven by the widespread adoption of AI-powered analytics platforms that offer advanced visualization, real-time processing, and predictive modeling capabilities. These software solutions are increasingly being integrated with existing business intelligence tools, enabling organizations to seamlessly incorporate heatmap analytics into their decision-making workflows. The continuous evolution of AI algorithms, coupled with user-friendly interfaces and customizable dashboards, is further boosting the adoption of software-based solutions across diverse industries.

The hardware segment is witnessing s

locations of trees in the Bronx according to the most recent tree census.

Heatmap Detection

## Overview

Heatmap Detection is a dataset for object detection tasks - it contains Vehicles Q5Ct annotations for 367 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Police station locations in Chicago