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#timeseries

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binjr

binjr v3.23.0 is now available! 🎉

The main feature for this release is an enhancement to the CSV files plugin to allow loading multiple files at once from a directory or directly from a zip archive, without the need to decompress it first!

It also leverages a new feature in the latest release of the #Java runtime to help reduce memory usage significantly, as well as bug fixes.

Full changelog and download links at https://binjr.eu

openjdk.orgJEP 450: Compact Object Headers (Experimental)
#javafx#dataviz#timeseries
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screwlisp

#programming #graphing #plotting #visualization #timeSeries #gnuplot #commonLisp #lisp #example screwlisp.small-web.org/progra
I could not even find my own previous articles and #demos of this online!

I used #uiop run-program to handle one specific case like

(gnuplot "bad title" '((1 2) (3 4)) '((5 6) (7 8)))
or equivalently,
(apply 'gnuplot "bad title" '(((1 2) (3 4)) ((5 6) (7 8))))

Do you personally have an example? I remember it being hard to dredge up gnuplot examples but this is beyond silly.

A plot of two differently colored line segments "with a bad title" with the y axis showing number increments.
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binjr

binjr v3.22.0 is now available! 🎉

There is now an option to further refine the behaviour of empty tab panes, introduced in the previous release.
This release also addresses a couple of bugs and brings all dependencies up to date (including #OpenJDK 24).

Full changelog and download links at binjr.eu

binjrbinjr - A Time Series Browserbinjr is a standalone time series browser. it renders time series data produced by other applications as dynamically editable charts and provides advanced features to navigate the data smoothly and efficiently.
#java#javafx#dataViz
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PaquitoBernard

I am looking for a postdoc 'Environmental and behavioral health in a changing climate' (1/2)

2 years in #Rennes #france #rstats #timeseries

We are looking for a postdoctoral researcher to help us understand the short-term impacts of environmental conditions on mental health, sleep and physical activity related behaviors. Future findings will help us better anticipate present and future consequences of climate change on bike use and sleep.

#academia#research#climatechange
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Thomas Arildsen

I have a #timeseries of values at low temporal resolution where the values represent an average of the respective surrounding intervals.
I wish to up-sample this sequence to a higher temporal resolution in such a way that the average of the up-sampled values is equal to the corresponding value from the original time series.
Does an #algorithm for the kind of interpolation I am looking for exist? (not Pandas' resample or SciPy's signal.resample.) And is there an implementation of it in #Python?

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Pierre-Simon Laplace

🔴 𝐇𝐨𝐰 𝐓𝐨 𝐅𝐨𝐜𝐮𝐬 𝐎𝐧 𝐖𝐡𝐚𝐭 𝐌𝐚𝐭𝐭𝐞𝐫𝐬 𝐈𝐧 𝐓𝐢𝐦𝐞 𝐒𝐞𝐫𝐢𝐞𝐬 𝐅𝐨𝐫𝐞𝐜𝐚𝐬𝐭𝐢𝐧𝐠
🔗 learnbayesstats.com/episode/12

✅ 𝐈𝐧 𝐭𝐡𝐢𝐬 𝐄𝐩𝐢𝐬𝐨𝐝𝐞, 𝐲𝐨𝐮’𝐥𝐥 𝐥𝐞𝐚𝐫𝐧 𝐡𝐨𝐰 𝐭𝐨 𝐚𝐜𝐡𝐢𝐞𝐯𝐞 𝐭𝐡𝐚𝐭 with @pymc

Alex Andorra & Jesse Grabowski talk about state space models, simplifying forecasting, applications etc.

#LearningBayesianStatistics#PyMC#forecasting
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💧🌏 Greg Cocks

Mapping Historical Forest Biomass For Stock-Change Assessments At Parcel To Landscape Scales
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doi.org/10.1016/j.foreco.2023. <-- shared paper
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phys.org/news/2022-06-historic <-- shared technical article
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dx.doi.org/10.1126/science.abk <-- shared paper
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#GIS #spatial #mapping #Landsat #LiDAR #AbovegroundBiomass #MachineLearning #LandCover #biomass #spatialanalysis #spatiotemporal #historical #parcel #landscape #Landtrendr #landsat #timeseries #machinelearning #algorithm #model #modeling #field #fieldwork #inventory #LiDAR #NewYork #forest #vegetation #carbonstocks #carbonaccounting #temporal #opendata #opensource #climatechange #planning #management

photo - base of a moss-covered tree in a forest
schematic - A flowchart diagram showing the key elements of the modeling and mapping methodology. Cylinders represent data repositories, parallelograms represent data products and results, rectangles represent processing steps, and ovals represent models.
maps - New York State (USA) AGB difference map (2019 AGB - 1990 AGB) with predictions from the ensemble model. a) Statewide scale. b) A mosaic of working forests and Adirondack Forest Preserve land south of Stillwater Reservoir, NYS. c) Allegany River area with a portion of Allegany State Park to the east of the river. d) Forest growth along the border of the Catskill Forest Preserve. Values are capped at ± 75 Mg ha−1 for display.
maps - A qualitative comparison of maps from each modeling approach within Huntington Wildlife Forest (boundary mapped with black box in NAIP panel) and the surrounding area in Newcomb, New York (full area extent mapped with black box in New York State panel). Pair-wise raster subtractions (values capped at ± 75 Mg ha−1 for display) highlight spatial patterns and magnitudes of differences between model predictions. Ensemble - Direct not shown because it duplicates Indirect - Ensemble. A 1 m LiDAR-derived canopy height model and 0.5 m natural color National Aerial Imagery Program (NAIP) orthophotography included for additional reference information. All surfaces represent conditions in 2015.
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💧🌏 Greg Cocks

Integrated Topographic Corrections Improve Forest Mapping Using Landsat Imagery
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doi.org/10.1016/j.jag.2022.102 <-- shared 2022 paper
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“HIGHLIGHTS:
• [They] evaluated the impacts of topographic correction on forest mapping in the mountains.
• The enhanced C-correction and the physical model reduced topographic effects.
• The corrected Landsat imagery time series resulted in higher accuracy.
• Terrain information improved classification but not as much as topographic correction.
• [They] recommend using topographic correction for forest cover mapping..."
#GIS #spatial #AtmosphericCorrection #IlluminationCondition #LandCover #ModelComparison #TimeSeries #TopographicCorrection #remotesensing #comparasion #topographic #correction #NDVI #forest #vegetation #model #modeling #spatialanalyis #accuracy #forestcover #Russia #Georgia #CaucasusMountains #spatiotemporal #landsat #elevation #DEM

graphic - Data processing workflow
photo - Mount Elbrus
maps / images - Landsat summer (panel A) and autumn (Panel B) images (RGB: 743) for the study area. Subset region marked in black frame. Panels C-F show uncorrected (panel C), corrected summer image using the enhanced C-correction (panel D) or the physical model (panel E), and the corresponding illumination condition (F). Panels G-J show the same as panel C-F, but for the autumn image. The QA layers generated from FORCE were not applied here.
map - Forest cover classification agreement among the 18 sets of input variables. Pixels in red color were classified by all sets of input variables as coniferous, in green color as broadleaf forest, and in blue color as mixed forest. Black color indicates that no forest was predicted by any set of input variables. Two subsets A and B which are marked in white frames were zoomed in for a detailed map comparison in [another figure]
A répondu dans un fil de discussion
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bsull

The forecasting implementations are also heavily based on the OG Python and R versions, there's not a tremendous amount of new stuff there! Outlier and clustering are a bit more interesting at least.

Anyway, issues and PRs are welcome if you find something missing!

#rustlang#timeseries
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💧🌏 Greg Cocks

Counting Up Eclipses
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maps.com/counting-up-eclipses/ <-- link to technical article
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bbc.co.uk/programmes/m000qmnj <-- a highly recommended podcast on the scientific study and history of solar eclipses (BBC, In Our Time)
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“A total solar eclipse captivated North America in April of 2024. Millions of people watched as daylight plunged into darkness. A luminous, almost otherworldly ring replaced the familiar view of the Sun. Totality had arrived. But just how common is such an experience? How frequently do total solar eclipses occur, and are there places on Earth more prone to eclipses than others?...”
#GIS #spatial #mapping #eclipse #eclipses #cartography #spatialanalysis #spatiotemporal #solareclipse #globe #global #history #totality #animation #timeseries

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