No Publication Abstract is Available

No Publication Abstract is Available

This is a heatmap (a graphical representation of data where the individual values contained in a matrix are represented as colors) of 2013 deer hunt kills within the California Department of Fish & Wildlife (CDFW) North Central Region (Region 2). The data was compiled from 2013 CDFW Automated Licensing Data System (ALDS) tables. Text descriptions from hunters were approximated and placed with geographic coordinates. The resulting point data was converted to a heatmap using Kernel Density Tool in ArcGIS 10.1

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

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

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

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

From 2016 to 2018, we surveyed the world’s largest natural history museum collections to begin mapping this globally distributed scientific infrastructure. The resulting dataset includes 73 institutions across the globe. It has:

• Basic institution data for the 73 contributing institutions, including estimated total collection sizes, geographic locations (to the city) and latitude/longitude, and Research Organization Registry (ROR) identifiers where available.

• Resourcing information, covering the numbers of research, collections and volunteer staff in each institution.

• Indicators of the presence and size of collections within each institution broken down into a grid of 19 collection disciplines and 16 geographic regions.

• Measures of the depth and breadth of individual researcher experience across the same disciplines and geographic regions.

This dataset contains the data (raw and processed) collected for the survey, and specifications for the schema used to store the data. It includes:

1. A diagram of the MySQL database schema.
2. A SQL dump of the MySQL database schema, excluding the data.
3. A SQL dump of the MySQL database schema with all data. This may be imported into an instance of MySQL Server to create a complete reconstruction of the database.
4. Raw data from each database table in CSV format.
5. A set of more human-readable views of the data in CSV format. These correspond to the database tables, but foreign keys are substituted for values from the linked tables to make the data easier to read and analyse.
6. A text file containing the definitions of the size categories used in the collection_unit table.

The global collections data may also be accessed at https://rebrand.ly/global-collections. This is a preliminary dashboard, constructed and published using Microsoft Power BI, that enables the exploration of the data through a set of visualisations and filters. The dashboard consists of three pages:

Institutional profile: Enables the selection of a specific institution and provides summary information on the institution and its location, staffing, total collection size, collection breakdown and researcher expertise.

Overall heatmap: Supports an interactive exploration of the global picture, including a heatmap of collection distribution across the discipline and geographic categories, and visualisations that demonstrate the relative breadth of collections across institutions and correlations between collection size and breadth. Various filters allow the focus to be refined to specific regions and collection sizes.

Browse: Provides some alternative methods of filtering and visualising the global dataset to look at patterns in the distribution and size of different types of collections across the global view.

This dataset contains information on vessel movements in the North Sea and Dutch Inland Waterways for the months January, April, July, and October in 2019. It provides a heatmap representation of vessel traffic density during these specific months, which can be useful for various maritime and environmental analyses.

1. File Formats

The dataset is provided in the following file formats:

• NetCDF : The primary data files are available in netcdf format. For each grid cell the variables sog (Speed Over Ground) and count (Number of AIS messages) are available
• GeoTIFF (Georeferenced Tagged Image File Format): Heatmap images are provided in GeoTIFF format, suitable for geographic visualization.

The dataset is split into tiles. Each tile conforms to the OSM tiling naming scheme.

2. Variables

The dataset includes the following key variables:

• Speed Over Ground (SOG): The average vessel's speed over the ground for all the messages.
• Count: The number of AIS messages received in this location

3. Data Collection Method

The AIS data used in this dataset was collected from AIS transponders on vessels operating in the North Sea and Dutch Inland Waterways. These transponders transmit information such as vessel position, speed, and identification. The dataset aggregates this information to create heatmap images for analysis. We did this on all the messages. Some ships emit more messages than others. Ships emit messages at higher frequency when sailing than when stationary.

4. Source of Original Data

The original AIS data used to create this dataset was sourced from the AIS archive from Rijkswaterstaat. This dataset was analysed for the purpose of a storymap.

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

The aim of this paper is to examine the current and potential capability to promote the green economy in Mexico, simultaneously detecting new opportunities for diversification and “green” productive sophistication so that Mexican entities can move toward environmentally friendly ecological products. For this, we adopted a novel methodology to measure the productive capabilities of the green economy in Mexico, thereby discovering the green product space at a subnational scale. Economic complexity methods were used to estimate the Green Complexity Index (GCI) and the Green Complexity Potential (GCP) for 32 Mexican regions considering a time series from 2004 to 2018 and a set of data on international trade in ecological products. The main findings are reflected in a grid of the Green Adjacent Possible (GAP) and a heatmap that shows the “grasslands” (current green products by state). The results are likely to influence industrial policy and state innovation agendas. A limitation of this work is that it is based only on data from the formal, industrial, and regulated economy. The originality lies in the fact that there were no previous studies in the context analyzed, and the fecundity of the research reflects the need to expand the study with a focus on green business models.

Important Note: This item is in mature support as of June 2023 and will be retired in December 2025.This map shows the total crime index in the U.S. in 2022 in a multi-scale map (by state, county, ZIP Code, tract, and block group). The layer uses 2020 Census boundaries.The pop-up is configured to include the following information for each geography level:Total crime indexPersonal and Property crime indices Sub-categories of personal and property crime indicesPermitted use of this data is covered in the DATA section of the EsriMaster Agreement (E204CW) and these supplemental terms.

Heatmap of the GEO database analysing the expression of intersecting genes across samples. (XLSX)