Data visualization is important for statistical analysis, as it helps convey information efficiently and shed lights on the hidden patterns behind data in a visual context. It is particularly helpful to display circular data in a two-dimensional space to accommodate its nonlinear support space and reveal the underlying circular structure which is otherwise not obvious in one-dimension. In this article, we first formally categorize circular plots into two types, either height- or area-proportional, and then describe a new general methodology that can be used to produce circular plots, particularly in the area-proportional manner, which in our opinion is the more appropriate choice. Formulas are given that are fairly simple yet effective to produce various circular plots, such as smooth density curves, histograms, rose diagrams, dot plots, and plots for multiclass data. Supplemental materials for this article are available online.

Hello all, this dataset involves various factors effecting cancer and based upon those factors, I have created a Histogram of various columns of the table which leads to heart disease. A histogram is a bar graph-like representation of data that buckets a range of outcomes into columns along the x-axis. The y-axis represents the number count or percentage of occurrences in the data for each column and can be used to visualize data distribution. At last I have created combined histogram of entire table which involves all the columns. Giving Titles, X-axis name, Y-axis name, Sizes and Colors is also done in this notebook.

Histogram plot of the average alignment accuracy averaged over 10 runs for each viral genome shown in Table 1 and each aligner. Reads crossing splice junction regions are shown in pink, reads not crossing splice junction regions are shown in blue).

Charts, Histograms, and Time Series • Create a histogram graph from band values of an image collection • Create a time series graph from band values of an image collection

Use the Chart Viewer template to display bar charts, line charts, pie charts, histograms, and scatterplots to complement a map. Include multiple charts to view with a map or side by side with other charts for comparison. Up to three charts can be viewed side by side or stacked, but you can access and view all the charts that are authored in the map. Examples: Present a bar chart representing average property value by county for a given area. Compare charts based on multiple population statistics in your dataset. Display an interactive scatterplot based on two values in your dataset along with an essential set of map exploration tools. Data requirements The Chart Viewer template requires a map with at least one chart configured. Key app capabilities Multiple layout options - Choose Stack to display charts stacked with the map, or choose Side by side to display charts side by side with the map. Manage chart - Reorder, rename, or turn charts on and off in the app. Multiselect chart - Compare two charts in the panel at the same time. Bookmarks - Allow users to zoom and pan to a collection of preset extents that are saved in the map. Home, Zoom controls, Legend, Layer List, Search Supportability This web app is designed responsively to be used in browsers on desktops, mobile phones, and tablets. We are committed to ongoing efforts towards making our apps as accessible as possible. Please feel free to leave a comment on how we can improve the accessibility of our apps for those who use assistive technologies.

The contains flight statistics for all airports in the United States from January 2011 to December 2020. Each observation is reported by month, year, airport, and airline. Flights can be categorized as on time, delayed, canceled, or diverted. Flight delays are attributed to five causes: carrier, weather, NAS, security, and late aircraft. The data was downloaded from the Bureau of Transportation Statistics website https://www.transtats.bts.gov/OT_Delay/OT_DelayCause1.asp.

The accompanying notebook explores commercial airplane flight delays in the United States using Python's visualization capabilities in Matplotlib and Seaborn, through the lenses of seasonality, airport traffic, and airline performance.

The clean data set (delays_clean.csv) is analyzed using the following visualizations:

Bar chart Bar chart subplots Lollipop chart Tree maps Line plot Histogram Histogram subplots Horizontal stacked bar chart Ranked horizontal bar chart Box plot Pareto chart - double axis Marginal histogram Pie charts Scatter plot Violin plot Map chart Linear regression

Figures containing a histogram of frequency of effect sizes on AG and BG herbivores and a funnel plot of effect size and sample sizes indicating absence of publication bias.

Figures in scientific publications are critically important because they often show the data supporting key findings. Our systematic review of research articles published in top physiology journals (n = 703) suggests that, as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies. Papers rarely included scatterplots, box plots, and histograms that allow readers to critically evaluate continuous data. Most papers presented continuous data in bar and line graphs. This is problematic, as many different data distributions can lead to the same bar or line graph. The full data may suggest different conclusions from the summary statistics. We recommend training investigators in data presentation, encouraging a more complete presentation of data, and changing journal editorial policies. Investigators can quickly make univariate scatterplots for small sample size studies using our Excel templates.

Geovisualizing the modifiable areal unit problem Interactive Google Earth Engine Application https://cartoscience.users.earthengine.app/view/maup Google Earth Engine Code var img = ee.Image('MODIS/055/MOD17A3/2014_01_01').select('Npp').divide(10000).rename('NPP'); Map.setCenter(30, 20, 2).setOptions('HYBRID'); var bounds = ee.Geometry(Map.getBounds(true)); var res = Map.getScale()*10; var colors = ['e0470e','e6ad0c','ffffc3','13ff92','079b5d']; var vis = {min: 1, max: 5, palette: colors}; var design = { fontSize: 10, legend: {position: 'none'}, backgroundColor: '080c16', series: {0: {color: 'e8e8e8', textStyle: {color: 'e8e8e8'}}}, hAxis: {format: 'short', textStyle: {color: 'e8e8e8'}}, vAxis: {format: 'short', textStyle: {color: 'e8e8e8'}} }; var histogram = ui.Chart.image.histogram(img, bounds, res).setOptions(design); var legend = ui.Panel( [ ui.Label('Low', {fontSize: '10px', fontWeight: 'bold', color: 'e8e8e8', stretch: 'horizontal', textAlign: 'center', backgroundColor: '080c16'}), ui.Label({style: {backgroundColor: 'e0470e', padding: '8px', margin: '6px 0 0 0'}}), ui.Label({style: {backgroundColor: 'e6ad0c', padding: '8px', margin: '6px 0 0 0'}}), ui.Label({style: {backgroundColor: 'ffffc3', padding: '8px', margin: '6px 0 0 0'}}), ui.Label({style: {backgroundColor: '13ff92', padding: '8px', margin: '6px 0 0 0'}}), ui.Label({style: {backgroundColor: '079b5d', padding: '8px', margin: '6px 0 0 0'}}), ui.Label('High', {fontSize: '10px', fontWeight: 'bold', color: 'e8e8e8', stretch: 'horizontal', textAlign: 'center', backgroundColor: '080c16'}), ], ui.Panel.Layout.flow('horizontal'), {width: '200px', position: 'bottom-left', backgroundColor: '080c16', margin: '-5px 0 10px 24px'} ); var panel = ui.Panel( [ ui.Label('Geovisualizing the Modifiable Areal Unit Problem', {fontSize: '20px', fontWeight: 'bold', color: 'e8e8e8', stretch: 'horizontal', textAlign: 'center', margin: '20px 0 10px 0', backgroundColor: '080c16'}), ui.Label('Pan, zoom, and adjust the browser window to reclassify.', {fontSize: '13px', color: 'e8e8e8', stretch: 'horizontal', textAlign: 'center', margin: '4px 25px 4px 25px', backgroundColor: '080c16'}), ui.Label('NASA MODIS Net Primary Productivity', {fontSize: '10px', color: 'e8e8e8', stretch: 'horizontal', textAlign: 'center', margin: '4px 20px 1px 20px', backgroundColor: '080c16'}), ui.Label('MOD17A3/055 • 2014', {fontSize: '10px', color: 'e8e8e8', stretch: 'horizontal', textAlign: 'center', margin: '1px 20px 1px 20px', backgroundColor: '080c16'}), ui.Label('kg C / m^2', {fontSize: '9px', color: 'e8e8e8', stretch: 'horizontal', textAlign: 'center', margin: '10px 20px 0 20px', backgroundColor: '080c16'}), histogram, legend, ui.Label('Recategorizes using percentile thresholds (0-20-40-60-80-100) from the data distribution captured in the frame. Zoom in to reveal heterogeneity at local scales.', {fontSize: '10px', color: 'e8e8e8', stretch: 'horizontal', textAlign: 'center', margin: '1px 20px 1px 20px', backgroundColor: '080c16'}), ui.Label('© 2019 Cartoscience', {fontSize: '10px', color: 'e8e8e8', stretch: 'horizontal', textAlign: 'center', margin: '20px 20px 0 20px', backgroundColor: '080c16'}), ui.Label('cartoscience.com', {fontSize: '10px', color: 'e8e8e8', stretch: 'horizontal', textAlign: 'center', margin: '1px 85px 20px 85px', backgroundColor: '080c16'}, 'http://cartoscience.com') ], ui.Panel.Layout.flow('vertical'), {width: '250px', position: 'bottom-left', backgroundColor: '080c16'} ); ui.root.insert(0, panel); Map.layers().set(0, ui.Map.Layer(ee.Image(0), {palette:'000000', opacity: 0.8}, 'Dark')); Map.onChangeBounds(function() { var bounds = ee.Geometry(Map.getBounds(true)); var res = Map.getScale()*10; var params = img.reduceRegion({ reducer: ee.Reducer.percentile([0,20,40,60,80,100]), geometry: bounds, scale: res }); var p0 = ee.Number(params.get('NPP_p0')); var p20 = ee.Number(params.get('NPP_p20')); var p40 = ee.Number(params.get('NPP_p40')); var p60 = ee.Number(params.get('NPP_p60')); var p80 = ee.Number(params.get('NPP_p80')); var p100 = ee.Number(params.get('NPP_p100')); var quintiles = img.gt(p0).add(img.gt(p20)).add(img.gt(p40)) .add(img.gt(p60)).add(img.gt(p80)).add(img.gt(p100)); var histogram = ui.Chart.image.histogram(img, bounds, res).setOptions(design); panel.widgets().set(5,histogram); Map.layers().set(1, ui.Map.Layer(quintiles, vis, 'Quintiles')); Map.layers().set(2, ui.Map.Layer(img, {min: 0.13, max: 0.79}, 'MODIS/055/MOD17A3', false)); }); This content is made possible by the support of the American People provided to the Feed the Future Innovation Lab for Sustainable Intensification through the United States Agency for International Development (USAID). The contents are the sole responsibility of the authors and do not necessarily reflect the views of USAID or the United States Government. Program activities are funded by USAID under Cooperative Agreement No. AID-OAA-L-14-00006.

The average enrichment at oriP and the integration site are normalized in relation to the reference site. Three independent experiments are shown for the three cell cycle fractions analyzed. For location of the PCR primer pairs see Figure 2. Because DS does not exhibit cell cycle-dependent binding of HsOrc2, the mean value and standard deviation of the fragments was used to establish the average enrichment (average). The mean value at the reference site, which represents the non-sequence specific DNA-binding activity of the analyzed protein, was determined and used for normalization to calculate the accumulation for oriP and the integration site. The obtained numbers represent the enrichment at the particular fragment above the reference site level. With the exception of ΔDS(p2910), the enrichment of HsMcm7 is cell cycle-dependent (cell cycle). Therefore no average value was determined.

Network of 29 papers and 63 citation links related to "Histogram-Based Image Retrieval Keyed by Normalized HSY Histograms and Its Experiments on a Pilot Dataset".

The Quality Warranty Management Market is estimated to be valued at USD 122.0 billion in 2025 and is projected to reach USD 472.6 billion by 2035, registering a compound annual growth rate (CAGR) of 14.5% over the forecast period.

Raw gaze, interview and other data from 50 secondary school students (grade 10 - 12) solving statical graph tasks: estimating or comparing the mean from histograms, case-value plots, (stacked) dotplots and horizontal histograms. It contains some processed data. Furthermore, it contains all relevant information needed to reproduce or replicate this data collection process, for example, the design of the data collection, html-files with the webpages that were used, letters to participants, sizes and screen shots of AOIs, heatmaps and static gazeplots. Also: transcripts, legends, overview of tasks. (Note, these data are not processed for a specific article)

THE FOLLOWING COMMENTS ARE TAKEN FROM THE PI N COMPILATION OF R.L. KELLY. THEY ARE THAT COMPILATION& apos;S COMPLETE SET OF COMMENTS FOR PAPERS RELATED TO THE SAME EXPERIMENT (DESIGNATED VANDEWAL68) AS THE CURRENT PAPER. (THE IDENTIFIER PRECEDING THE REFERENCE AND COMMENT FOR EACH PAPER IS FOR CROSS-REFERENCING WITHIN THESE COMMENTS ONLY AND DOES NOT NECESSARILY AGREE WITH THE SHORT CODE USED ELSEWHERE IN THE PRESENT COMPILATION.) /// VANDEWAL68 [R. T. VAN DE WALLE,NC 53,745(1968).] -- DATA READ FROM HISTOGRAM. PI- P DCS AT 848 MEV/C FROM .8K ELASTIC EVENTS IN SACLAY 35 CM HBC AT SATURNE. THE DATA IS PRESENTED AS LEGENDRE COEFFICIENTS WITH A FULL ERROR MATRIX AND IN A SMALL HISTOGRAM BASED ON .6K EVENTS. WE USED THE HISTOGRAM. /// COMMENTS FROM LOVELACE71 COMPILATION OF THESE DATA -- NORMALISED TO TOT=37.2(DEVLIN). READ FROM GRAPH. /// COMMENTS ON MODIFICATIONS TO LOVELACE71 COMPILATION BY KELLY -- IN REREADING THE HISTOGRAM WE FOUND ONE MAJOR MISTAKE IN THE LOVELACE71 VERSION (LOVELACE ID=WALLE68).. DATA ARE UNNORMALIZED OR NORMALIZED TO OTHER DATA.

FOCUSON**LONDON**2011: HOUSING:A**GROWING**CITY

With the highest average incomes in the country but the least space to grow, demand for housing in London has long outstripped supply, resulting in higher housing costs and rising levels of overcrowding. The pressures of housing demand in London have grown in recent years, in part due to fewer people leaving London to buy homes in other regions. But while new supply during the recession held up better in London than in other regions, it needs to increase significantly in order to meet housing needs and reduce housing costs to more affordable levels.

This edition of Focus on London authored by James Gleeson in the Housing Unit looks at housing trends in London, from the demand/supply imbalance to the consequences for affordability and housing need.

REPORT:

Read the report in PDF format.

https://londondatastore-upload.s3.amazonaws.com/fol/fol11-housing-cover-thumb.jpg" alt="">

PRESENTATION:

How much pressure is London’s popularity putting on housing provision in the capital? This interactive presentation looks at the effect on housing pressure of demographic changes, and recent new housing supply, shown by trends in overcrowding and house prices. Click on the start button at the bottom of the slide to access.

View Focus on London - Housing: A Growing City on Prezi

HISTOGRAM:

This histogram shows a selection of borough data and helps show areas that are similar to one another by each indicator.

Histogram

MOTION CHART:

This motion chart shows how the relationship, between key housing related indicators at borough level, changes over time.

Motion Chart

MAP:

These interactive borough maps help to geographically present a range of housing data within London, as well as presenting trend data where available.

MAP

DATA:

All the data contained within the Housing: A Growing City report as well as the data used to create the charts and maps can be accessed in this spreadsheet.

FACTS:

Some interesting facts from the data…

● Five boroughs with the highest proportion of households that have lived at their address for less than 12 months in 2009/10:

1. Westminster – 19 per cent
2. Wandsworth – 17 per cent
3. Camden – 16 per cent
4. Lambeth – 14 per cent
5. Southwark – 13 per cent

-31. Harrow – 6 per cent

-32. Havering – 5 per cent

● Five boroughs with the highest percentage point increase between 2004 and 2009 of households in the ‘private rented’ sector:

1. Newham – 17 per cent
2. Greenwich – 11 per cent
3. Enfield – 10 per cent
4. Camden – 9 per cent
5. Harrow – 8 per cent

-32. Islington – 1 per cent

-33. Bexley – 1 per cent

● Five boroughs with the highest percentage difference in median house prices between 2007 Q4 and 2010 Q4:

1. Kensington & Chelsea – 29 per cent
2. Westminster – 19 per cent
3. Camden – 15 per cent
4. Islington – 14 per cent
5. Southwark – 10 per cent

-31. Newham – down 9 per cent

-32. Barking & D’ham – down 9 per cent

As part of a 3-year project examining both saline (Task 2) and hydrocarbon (Task 3) reservoirs to better understand the storage coefficients of deep saline storage and utilization factors or enhanced oil recovery, intensive literature reviews were performed to gather necessary data to build and simulate geocellular models. The following outlines the methodology, data collected, and references for Task 2.

In the attached spreadsheet, each tab represents a formation(s) and the primary depositional environment. Within each tab, the data are displayed for easy consumption. A header provides basic information such as formation, basin and country, environment, and data origin. Below the header are the collected porosity and permeability values. Each row is depth-oriented; thus the porosity listed correlates directly to the permeability. On the right side of the page, four charts are shown. The top chart is a porosity histogram with statistics, the second chart is a permeability histogram with statistics, the third chart is a porosity vs. permeability crossplot for the formation selected, and the fourth chart is a porosity vs. permeability crossplot from the AGD representing the same environment.

Histogram plot of the average accuracy as a function of the mutation rate for each aligner averaged over the four viral genomes (see Table 1).

This repository entails the data and Pythoncode for the publication "Dynamics of Lagrangian Sensor Particles: The Effect of Non-Homogeneous Mass Distribution" in the journal "Processes". In the following a brief introduction and guide based on the folders in the repository is laid out. More code specific instructions can be found in the respective codes. 01 --> The tracking always begins with the same 01_milti[...] folder in which the python code with OpenCV algorithm is located. For tracking the tracking to work certain directories are required in which the raw images are to be stored (separate from anything else) as well as a directory in which the results are to be save (not the same directory as the raw data). After tracking is completed for all respective experiments and the results directories are adequately labelled and stored any of the other code files can be used for respective analyses. The order of folders beyond the first 01 directory has no relevance to the order of evaluation however can ease the understanding of evaluated data if followed. 02 --> Evaluation of amount of circulations and respective circulation time in experimental vat. (code can be extended to calculate the circulation time based on the various plains that are artificially set) 03 --> Code for the calculation of the amount of contacts with the vat floor. Code requires certain visual evaluations based on the LP trajectories, as the plain/barrier for the contact evaluation has to be manually set. 04 --> Contains two codes that can be applied to results data to combine individual results into larger more processable arrays within python 05 --> Contains the code to plot the trajectory of single experiments of Lagrangian particles based on their positional results and velocity at respective position, highlighting the trajectory over the experiment. 06 --> Condes to create 1D histograms based on the probability density distribution and velocity distributions in cumulative experiments. 07 --> Codes for plotting the 2D probability density distribution (2D Histograms) of Lagrangian Particles based on the cumulative experiments. Code provides values for the 2D grid, plotting is conducted in Origin Lab or similar graphing tools, graphing can also be conducted in python whereby the seaborn (matplotlib) library is suggested. 08 --> Contain the code for the dimensionless evaluation of the results based on the respective Stokes number approaches and weighted averages. 2D histograms are also vital to this evaluation, whereby the plotting is again conducted in Origin Lab as values are only calculated in code. 09 --> Directory does not contain any python codes but instead contains the respective Origin Lab files for the graphing, plotting and evaluation of results calculated via python is given. Respective tables, histograms and heat maps are hereby given to be used as templates if necessary. The project used the Origin 2023 (64-bit) version, if no Origin license is available then Origin Lab provides a free Origin Viewer with which the projects can be opened and viewed. (https://www.originlab.com/viewer/)

Each table (CSV file) captures the numbers of counts of arriving energetic neutral atoms, as integrated over a full 8-day orbit, for a single IBEX-Lo energy step, for one of two categories of species (hydrogen, oxygen plus carbon, the latter being labelled "Oxygen" in file names).
Numbers of counts are given in 360 1-degree bands in spacecraft spin phase. Each table is CSV-formatted Excel.
Each subdirectory is for a given energy band and has the tables (files) for each of several orbits for each of the two species categories.

THE FOLLOWING COMMENTS ARE TAKEN FROM THE PI N COMPILATION OF R.L. KELLY. THEY ARE THAT COMPILATION& apos;S COMPLETE SET OF COMMENTS FOR PAPERS RELATED TO THE SAME EXPERIMENT (DESIGNATED NEWCOMB63) AS THE CURRENT PAPER. (THE IDENTIFIER PRECEDING THE REFERENCE AND COMMENT FOR EACH PAPER IS FOR CROSS-REFERENCING WITHIN THESE COMMENTS ONLY AND DOES NOT NECESSARILY AGREE WITH THE SHORT CODE USED ELSEWHERE IN THE PRESENT COMPILATION.) /// NEWCOMB63 [P. C. A. NEWCOMB,PR 132,1283(1963).] -- PI+ P DCS AT 725 MEV/C FROM 1245 ELASTIC EVENTS IN THE 15 INCH LRL HBC AT THE BEVATRON. DATA PRESENTED AS A TABLE OF NUMBERS OF EVENTS AND A HISTOGRAM NORMALIZED TO A TOTAL CS OF 16.1+/-0.8 MB. THE MB RECORDED HERE INCLUDES THE SPREAD IN BEAM MOMENTUM OVER THE FIDUCIAL VOLUME. /// NEWCOMB63T [P. C. A. NEWCOMB,UCB THESIS,UCRL-10563,1963.] -- A LARGER VESION OF THE HISTOGRAM IS GIVEN HERE. WE USED THE NORMALIZATION READ FROM THIS HISTOGRAM, 1 EVENT/ .1 COS(THETA) INTERVAL=.0141 MB/STER, TO NORMALIZE THE TABLE IN NEWCOMB63. /// COMMENTS ON MODIFICATIONS TO LOVELACE71 COMPILATION BY KELLY -- WE NORMALIZED THE TABLE IN NEWCOMB63 AS DESCRIBED ABOVE, RESULTING IN ONLY MINOR DIFFERENCES FROM THE LOVELACE71 VERSION WHICH WAS APPARENTLY READ DIRECTLY FROM THE HISTOGRAM IN NEWCOMB63.. DATA ARE UNNORMALIZED OR NORMALIZED TO OTHER DATA.