EDAI 4 Vehicle Density Analysis

## Overview

EDAI 4 Vehicle Density Analysis is a dataset for object detection tasks - it contains Ambulance Motorcycles annotations for 695 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).

Original data can be downloaded from here. Another online version of the data can be found HERE.This version presented and hosted by CPAWS-NL allows for data extraction and analysis within ArcGIS Online Map Viewer."Kernel density estimation (KDE) utilizes spatially explicit data to model the distribution of a variable of interest. It is a simple non-parametric neighbor-based smoothing function that relies on few assumptions about the structure of the observed data. It has been used in ecology to identify hotspots, that is, areas of relatively high biomass/abundance, and in 2010 was used by Fisheries and Oceans Canada to delineate significant concentrations of corals and sponges. The same approach has been used successfully in the Northwest Atlantic Fisheries Organization (NAFO) Regulatory Area. Here, we update the previous analyses with the catch records from up to 5 additional years of trawl survey data from Eastern Canada, including the Gulf of St. Lawrence. We applied kernel density estimation to create a modelled biomass surface for each of sponges, small and large gorgonian corals, and sea pens, and applied an aerial expansion method to identify significant concentrations of theses taxa. We compared our results to those obtained previously and provided maps of significant concentrations as well as point data co-ordinates for catches above the threshold values used to construct the significant area polygons. The borders of the polygons can be refined using knowledge of null catches and species distribution models of species presence/absence and/or biomass." (DOI: 10.17632/dtk86rjm86.2)

This dataset shows population details by country. Population density is a measurement of population per unit area, or exceptionally unit volume; it is a quantity of type number density. There are two columns for density: by square km and by square miles.

This webmap is a subset of Global Urban Density Footprint in 2020 Tile Image Layer. This layer represents an estimate of the footprint of urban settings in 2020. It is intended as a fast-drawing cartographic layer to augment base maps and to focus a map reader's attention on the location of human population. This layer is not intended for analysis. This layer was derived from the 2020 slice of the WorldPop Population Density 2000-2020 100m and 1km layers.Also see the Populated Footprint layer, which like this layer, is intended to provide a fast-drawing cartographic context for the footprint of total population.The following processing steps were used to produce this layer in ArcGIS Pro:1. Int tool (Spatial Analyst) to truncate double precision values; all values less than 0.99 become 0.2. Reclassify tool (Spatial Analyst) to set values 0 through 1499 to NoData (Null) and all other values become 1.3. Copy Raster tool with Output Coordinate System environment set to Web Mercator, bit depth to 1 bit, and NoData Value to 0.Source:WorldPop Population Density 2000-2020 100m, which is created from WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation. The DOI for the original WorldPop.org total population population data is 10.5258/SOTON/WP00645.

The Density Meter Market report segments the industry into By Type (Benchtop, Module, Portable), By Application (Coriolis, Nuclear, Ultrasonic, Microwave, and more), By End-user Industry (Water and Wastewater, Chemicals, Mining and Metal Processing, and more), and By Geography (North America, Europe, Asia, Australia and New Zealand, Latin America, Middle East and Africa).

Analysis of ‘C0107 - Population Density and Area Size’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/44bd9b44-4a36-473a-adf9-9a72f0c24595 on 18 January 2022.

--- Dataset description provided by original source is as follows ---

Population Density and Area Size

--- Original source retains full ownership of the source dataset ---

Wet bulk density values obtained for the mid to late Pliocene composite section at ODP Site 138-850, determined by shipboard measurements using the GRAPE device. Values are reported as wet bulk density and also as detrended and normalized values.

It shows wood stork colony and nest density has changed over time. Within the breeding range, wood stork colonies and nest numbers generally cluster into four areas in the southeastern U.S. To identify these clusters, we conducted a density analysis and looked at colonies and nesting numbers at colony locations. A wood stork colony location map depicting nesting densities was generated through a GIS analysis that clustered colony location points into a specified neighborhood for each of the past 5 decades. The wood stork colony locations and nesting records show the breeding range expansion north from FL into GA, SC, and NC.

This map features the World Population Density Estimate 2016 layer for the Caribbean region. The advantage population density affords over raw counts is the ability to compare levels of persons per square kilometer anywhere in the world. Esri calculated density by converting the the World Population Estimate 2016 layer to polygons, then added an attribute for geodesic area, which allowed density to be derived, and that was converted back to raster. A population density raster is better to use for mapping and visualization than a raster of raw population counts because raster cells are square and do not account for area. For instance, compare a cell with 185 people in northern Quito, Ecuador, on the equator to a cell with 185 people in Edmonton, Canada at 53.5 degrees north latitude. This is difficult because the area of the cell in Edmonton is only 35.5% of the area of a cell in Quito. The cell in Edmonton represents a density of 9,810 persons per square kilometer, while the cell in Quito only represents a density of 3,485 persons per square kilometer. Dataset SummaryEach cell in this layer has an integer value with the estimated number of people per square kilometer likely to live in the geographic region represented by that cell. Esri additionally produced several additional layers: World Population Estimate 2016: this layer contains estimates of the count of people living within the the area represented by the cell. World Population Estimate Confidence 2016: the confidence level (1-5) per cell for the probability of people being located and estimated correctly. World Settlement Score 2016: the dasymetric likelihood surface used to create this layer by apportioning population from census polygons to the settlement score raster.To use this layer in analysis, there are several properties or geoprocessing environment settings that should be used:Coordinate system: WGS_1984. This service and its underlying data are WGS_1984. We do this because projecting population count data actually will change the populations due to resampling and either collapsing or splitting cells to fit into another coordinate system. Cell Size: 0.0013474728 degrees (approximately 150-meters) at the equator. No Data: -1Bit Depth: 32-bit signedThis layer has query, identify, pixel, and export image functions enabled, and is restricted to a maximum analysis size of 30,000 x 30,000 pixels - an area about the size of Africa.Frye, C. et al., (2018). Using Classified and Unclassified Land Cover Data to Estimate the Footprint of Human Settlement. Data Science Journal. 17, p.20. DOI: https://doi.org/10.5334/dsj-2018-020.What can you do with this layer?This layer is primarily intended for cartography and visualization, but may also be useful for analysis, particularly for estimating where people living above specified densities. There are two processing templates defined for this layer: the default, "World Population Estimated 2016 Density Classes" uses a classification, described above, to show locations of levels of rural and urban populations, and should be used for cartography and visualization; and "None," which provides access to the unclassified density values, and should be used for analysis. The breaks for the classes are at the following levels of persons per square kilometer:100 - Rural (3.2% [0.7%] of all people live at this density or lower) 400 - Settled (13.3% [4.1%] of all people live at this density or lower)1,908 - Urban (59.4% [81.1%] of all people live at this density or higher)16,978 - Heavy Urban (13.0% [24.2%] of all people live at this density or higher)26,331 - Extreme Urban (7.8% [15.4%] of all people live at this density or higher) Values over 50,000 are likely to be erroneous due to spatial inaccuracies in source boundary dataNote the above class breaks were derived from Esri's 2015 estimate, which have been maintained for the sake of comparison. The 2015 percentages are in gray brackets []. The differences are mostly due to improvements in the model and source data. While improvements in the source data will continue, it is hoped the 2017 estimate will produce percentages that shift less.For analysis, Esri recommends using the Zonal Statistics tool or the Zonal Statistics to Table tool where you provide input zones as either polygons, or raster data, and the tool will summarize the average, highest, or lowest density within those zones.

Data Sources: Banque informatisée des oiseaux de mer au Québec (BIOMQ: ECCC-CWS Quebec Region) Atlantic Colonial Waterbird Database (ACWD: ECCC-CWS Atlantic Region).. Both the BIOMQ and ACWD contain records of individual colony counts, by species, for known colonies located in Eastern Canada. Although some colonies are censused annually, most are visited much less frequently. Methods used to derive colony population estimates vary markedly among colonies and among species. For example, census methods devised for burrow-nesting alcids typically rely on ground survey techniques. As such, they tend to be restricted to relatively few colonies. In contrast, censuses of large gull or tern colonies, which are geographically widespread, more appropriately rely on a combination of broad-scale aerial surveys, and ground surveys at a subset of these colonies. In some instances, ground surveys of certain species are not available throughout the study area. In such cases, consideration of other sources, including aerial surveys, may be appropriate. For example,data stemming from a 2006 aerial survey of Common Eiders during nesting, conducted by ECCC-CWS in Labrador, though not yet incorporated in the ACWD, were used in this report. It is important to note that colony data for some species, such as herons, are not well represented in these ECCC-CWS databases at present. Analysis of ACWD and BIOMQ data (ECCC-CWS Quebec and Atlantic Regions): Data were merged as temporal coverage, survey methods and geospatial information were comparable. Only in cases where total counts of individuals were not explicitly presented was it necessary to calculate proxies of total counts of breeding individuals (e.g., by doubling numbers of breeding pairs or of active nests). Though these approaches may underestimate the true number of total individuals associated with a given site by failing to include some proportion of the non-breeding population (i.e., visiting adult non-breeders, sub-adults and failed breeders), tracking numbers of breeding individuals (or pairs) is considered to be the primary focus of these colony monitoring programs.In order to represent the potential number of individuals of a given species that realistically could be and may historically have been present at a given colony location (see section 1.1), the maximum total count obtained per species per site since 1960 was used in the analyses. In the case of certain species,especially coastal piscivores (Wires et al. 2001; Cotter et al. 2012), maxima reached in the 1970s or 1980s likely resulted from considerable anthropogenic sources of food, and these levels may never be seen again. The effect may have been more pronounced in certain geographic areas. Certain sites once used as colonies may no longer be suitable for breeding due to natural and/or human causes, but others similarly may become suitable and thus merit consideration in long-term habitat conservation planning. A colony importance index (CII) was derived by dividing the latter maximum total count by the potential total Eastern Canadian breeding population of that species (the sum of maximum total counts within a species, across all known colony sites in Eastern Canada). The CII approximates the proportion of the total potential Eastern Canadian breeding population (sum of maxima) reached at each colony location and allowed for an objective comparison among colonies both within and across species. In some less-frequently visited colonies, birds (cormorants, gulls, murres and terns, in particular) were not identified to species. Due to potential biases and issues pertaining to inclusion of these data, they were not considered when calculating species’ maximum counts by colony for the CII. The IBA approach whereby maximum colony counts are divided by the size of the corresponding actual estimated population for each species (see Table 3.1.2; approximate 1% continental threshold presented) was not used because in some instances individuals were not identified to species at some sites, or population estimates were unavailable.Use of both maxima and proportions of populations (or an index thereof) presents contrasting, but complementary, approaches to identifying important colonial congregations. By examining results derived from both approaches, attention can be directed at areas that not only host large numbers of individuals, but also important proportions of populations. This dual approach avoids attributing disproportionate attention to species that by their very nature occur in very large colonies (e.g., Leach’s Storm Petrel) or conversely to colonies that host important large proportions of less-abundant species (Roseate Tern, Caspian Tern, Black-Headed Gull, etc.), but in smaller overall numbers. Point Density Analysis (ArcGIS Spatial Analyst) with kernel estimation, and a 10-km search radius,was used to generate maps illustrating the density of colony measures (i.e., maximum count by species,CII by species), modelled as a continuous field (Gatrell et al. 1996). Actual colony locations were subsequently overlaid on the resulting cluster map. Sites not identified as important should not be assumed to be unimportant.

Datasets and R scripts from González-Suárez, M; Gonzalez-Voyer, A; von Hardenberg, A; Santini, L (2021) The role of brain size on mammalian population densities Journal of Animal Ecology, 90: 653– 661. DOI: 10.1111/1365-2656.13397Additional details in the README.pdf file

SUMMARY OF FILES INCLUDED

• CSV datasets: total 21 files representing

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12 csv datasets from other sources (described below) with brain and body mass data in the zip file Brain and Mass data.

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Six csv datasets from other sources and compilations (described below) with population density and diet information

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Two csv files (Complete_dataset_published.csv, Brain_data_compilation_published.csv) produced during this study. Details of the files and the compilation protocol are provided in the README file.

• R scripts: three scripts that describe the protocols to: combine brain and body mass data (brain_data_compilation_published.Rmd), combine all sources (Joining_all_data_published.Rmd), and analyse and produce tables and figures presented in the manuscript (Data_analysesPPA_published.Rmd).

This dataset is about books. It has 2 rows and is filtered where the book is Density estimation for statistics and data analysis. It features 7 columns including author, publication date, language, and book publisher.

Aim: Global animal populations are in decline due to destruction and degradation of their natural habitat. Understanding the factors that determine the distribution and density of threatened animal populations is therefore now a crucial component of their study and conservation. The Cheirogaleidae are a diverse family of small-bodied, nocturnal lemurs that are widespread throughout the forests of Madagascar. However, many cheirogaleid lemurs are now highly threatened with extinction and the environmental factors that determine their distribution and population density are still little known. Here, I investigated the environmental drivers of Cheirogaleidae population density at genus level.

Location: Various forest sites across Madagascar.

Methods: I investigated how six environmental variables affect Cheirogaleidae population density at the genus level via random effect meta-analyses. I then used a Generalized Linear Mixed-effects Model to identify the primary predictors of Cheirogale...

The attraction of tourism resources is very important to promote the sustainable development of tourism industry. This study takes China’s world cultural and natural heritage as the research object, and constructs an attractiveness evaluation system for China’s world cultural and natural heritage tourism resources by collecting user feedback data from three major travel OTA platforms. At the same time, ArcGIS 10.7 software was used for spatial autocorrelation analysis and kernel density analysis to explore the spatial distribution pattern of tourism resource attraction. The results show that China’s world cultural and natural heritage can be subdivided into 5 main categories and 10 sub-categories. From the perspective of spatial aggregation, only the Moran’s I index of tourist resource points showing a significant spatial aggregation feature. This study is helpful to reveal the weaknesses of tourism resource points and provide reference for sustainable development of attraction and optimization of tourism planning and management.

To prepare for its third phase, the Hawaii Play Fairway project conducted groundwater sampling and analyses in ten locations in the Hawaiian islands, magnetotelluric (MT) and gravity surveys, as well as calculations of 3D subsurface stress due to the weight of the rock underlying the topography of the volcano. The subsurface stresses were used to evaluate the potential for fracture-induced permeability. Inversions of the MT and gravity data produce 3D models of resistivity and density, respectively, on Lanai, across Haleakala's SW rift (Maui), and surrounding Mauna Kea (Hawaii Island). The project developed and applied a new method for incorporating depth information about resistivity, density, and potential for fracture-induced permeability into the statistical method for computing resource probability in these three focus areas. The project then incorporated the new groundwater results with the new geophysical results and the calculations of potential for fracture-induced permeability to produce updated maps of resource probability and confidence. These results were used to identify target sites for exploratory drilling. Spreadsheet information: Each sheet contains data for a particular depth in kilometers. Positive depths are above sea level, and negative below. For more information, go to the Hawaii Groundwater and Geothermal Resources Center website linked in the resources.

The experimental electron density study of Ti(C5H4Me)2[(CH2)2CMe2] provides direct evidence for the presence of (C−C)→Ti agostic interactions. In accord with the model of Scherer and McGrady, the Cα−Cβ bond densities no longer show cylindrical symmetry in the vicinity of the Ti atom and differ markedly from those of the other C−C bonds. At the points along the Cα−Cβ bond where the deviation is maximal the electron density is elongated toward the metal center. The distortion is supported by parallel theoretical calculations. A calculation on an Mo complex in which the agostic interaction is absent supports the Scherer and McGrady criterion for agostic interactions. Despite the formal d0 electron configuration for this Ti(IV) species, a significant nonzero population is observed for the d orbitals, the d orbital population is largest for the dxy orbital, the lobes of which point toward the two Cα atoms. Of the three different basis sets for the Ti atom used in theoretical calculations with the B3LYP functional, only the 6-311++G** set for Ti agrees well with the experimental charge density distribution in the Ti−(Cα−Cβ)2 plane.

The global market for Automatic Solid Density Analyzers is experiencing robust growth, driven by increasing demand across diverse sectors like pharmaceuticals, chemicals, and battery manufacturing. Precise density measurement is crucial for quality control and process optimization in these industries, fueling adoption of automated analyzers over traditional methods. The market, valued at approximately $150 million in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033. This growth is underpinned by several key factors. Technological advancements leading to higher accuracy, improved speed, and easier operation of these analyzers are significantly impacting market expansion. Furthermore, the rising focus on regulatory compliance and stringent quality standards in various industries is driving the adoption of these sophisticated instruments. The segment encompassing ultrasound-based analyzers currently holds the largest market share, followed by microwave and gravitic types. However, innovations in gravitic technologies are expected to increase their market share in the coming years. Geographically, North America and Europe currently dominate the market, but the Asia-Pacific region, particularly China and India, is anticipated to witness significant growth driven by industrialization and expanding manufacturing capabilities. While the market faces restraints such as high initial investment costs and the need for skilled operators, these are being offset by the long-term benefits of improved efficiency and accuracy. The competitive landscape is characterized by both established players like Anton Paar and Micromeritics Instruments and emerging companies specializing in niche technologies. These companies are focusing on product innovation, strategic partnerships, and geographic expansion to strengthen their market positions and cater to the growing demand for sophisticated density measurement solutions. The forecast period suggests a steady expansion of the market, with significant potential for further growth driven by continuous advancements in analyzer technology and its adoption across an expanding range of applications. The ongoing development of miniaturized and portable analyzers is likely to contribute to market penetration in smaller laboratories and field applications.

This dataset is about: Carbon and density analysis of sediment core PS1243-1.

This service is available to all ArcGIS Online users with organizational accounts. For more information on this service, including the terms of use, visit us at http://goto.arcgisonline.com/landscape7/World_Population_Density_Estimate_2016.This layer is a global estimate of human population density for 2016. The advantage population density affords over raw counts is the ability to compare levels of persons per square kilometer anywhere in the world. Esri calculated density by converting the the World Population Estimate 2016 layer to polygons, then added an attribute for geodesic area, which allowed density to be derived, and that was converted back to raster. A population density raster is better to use for mapping and visualization than a raster of raw population counts because raster cells are square and do not account for area. For instance, compare a cell with 185 people in northern Quito, Ecuador, on the equator to a cell with 185 people in Edmonton, Canada at 53.5 degrees north latitude. This is difficult because the area of the cell in Edmonton is only 35.5% of the area of a cell in Quito. The cell in Edmonton represents a density of 9,810 persons per square kilometer, while the cell in Quito only represents a density of 3,485 persons per square kilometer. Dataset SummaryEach cell in this layer has an integer value with the estimated number of people per square kilometer likely to live in the geographic region represented by that cell. Esri additionally produced several additional layers: World Population Estimate 2016: this layer contains estimates of the count of people living within the the area represented by the cell. World Population Estimate Confidence 2016: the confidence level (1-5) per cell for the probability of people being located and estimated correctly. World Settlement Score 2016: the dasymetric likelihood surface used to create this layer by apportioning population from census polygons to the settlement score raster.To use this layer in analysis, there are several properties or geoprocessing environment settings that should be used:Coordinate system: WGS_1984. This service and its underlying data are WGS_1984. We do this because projecting population count data actually will change the populations due to resampling and either collapsing or splitting cells to fit into another coordinate system. Cell Size: 0.0013474728 degrees (approximately 150-meters) at the equator. No Data: -1Bit Depth: 32-bit signedThis layer has query, identify, pixel, and export image functions enabled, and is restricted to a maximum analysis size of 30,000 x 30,000 pixels - an area about the size of Africa.What can you do with this layer?This layer is primarily intended for cartography and visualization, but may also be useful for analysis, particularly for estimating where people living above specified densities. There are two processing templates defined for this layer: the default, "World Population Estimated 2016 Density Classes" uses a classification, described above, to show locations of levels of rural and urban populations, and should be used for cartography and visualization; and "None," which provides access to the unclassified density values, and should be used for analysis. The breaks for the classes are at the following levels of persons per square kilometer:100 - Rural (3.2% [0.7%] of all people live at this density or lower) 400 - Settled (13.3% [4.1%] of all people live at this density or lower)1,908 - Urban (59.4% [81.1%] of all people live at this density or higher)16,978 - Heavy Urban (13.0% [24.2%] of all people live at this density or higher)26,331 - Extreme Urban (7.8% [15.4%] of all people live at this density or higher) Values over 50,000 are likely to be erroneous due to spatial inaccuracies in source boundary dataNote the above class breaks were derived from Esri's 2015 estimate, which have been maintained for the sake of comparison. The 2015 percentages are in gray brackets []. The differences are mostly due to improvements in the model and source data. While improvements in the source data will continue, it is hoped the 2017 estimate will produce percentages that shift less.For analysis, Esri recommends using the Zonal Statistics tool or the Zonal Statistics to Table tool where you provide input zones as either polygons, or raster data, and the tool will summarize the average, highest, or lowest density within those zones.

Analysis of ‘CD115 - Population Density and Area Size’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/92d6e6db-e8f4-44f0-8eb3-f105dc015760 on 19 January 2022.

--- Dataset description provided by original source is as follows ---

Population Density and Area Size

--- Original source retains full ownership of the source dataset ---