Tachylite

Spatiotemporal weighted value distribution map

Spatial Interpolation:
https://www.mdpi.com/1424-8220/16/8/1245
https://www.mdpi.com/2076-3417/15/6/2959

distribution-based distance weighing
Time-Decay Weighted Heatmap
Geospatial Weighted Surface

ChatGPT-Implementierungen:

Spatio-Temporal KDE (nicht value weighted):

https://tilmandavies.github.io/sparr/reference/spattemp.density.html
https://search.r-project.org/CRAN/refmans/sparr/html/spattemp.density.html
https://github.com/L-Koren/wSTKDE
https://github.com/alexandster/densitySpaceTime

Referenzen

https://arxiv.org/abs/2006.00272
https://arxiv.org/abs/2203.08317
https://jeremygelb.github.io/spNetwork/reference/tnkde.html
https://cran.r-project.org/web/packages/spNetwork/vignettes/TNKDE.html

Etwas:
https://www.sciencedirect.com/science/article/pii/S2215016124000591
Heatmap: https://www.mdpi.com/2072-4292/15/2/458
https://rmets.onlinelibrary.wiley.com/doi/10.1002/qj.5060
https://ieeexplore.ieee.org/abstract/document/4021339/
SURFER von Golden Software
Nadaraya–Watson kernel regression:
https://bookdown.org/egarpor/PM-UC3M/npreg-kre.html#npreg-kre-nw
https://mgimond.github.io/Spatial/chp16_0.html
Kriging: https://www.researchgate.net/publication/321642515_Kriging_Methods_and_Applications

Leaflet plugins:

ABA-Portal-Sachen:

  • Titel: 'Theory of Weighted Value Distribution Maps including the comparison of Spatiotemporal Smoothing Methods and a practical implementation using WebGL'
  • Untersuchungsanliegen: Theory of Spatiotemporal Weighted Value Distribution Maps including the comparison of Spatiotemporal Smoothing Methods and implementation using WebGL on top of Leaflet
  • Ergebnisse: The goal of this thesis is a Comprehensive understanding of different Spatiotemporal Smoothing Methods and the selection of one to be used in our project. The selected method is implemented using WebGL on top of the Leaflet Map Library

Struktur?:

  • Theorie
    • Setup (Datentypen, Erwünschtes Ergebnis) (1)
    • Namen für das Ding (ChatGPT) (mehrere Prompts, neu Prompten, zusätzlich die Quellen und existierende Implementierungen, Woher der Name kommt) (2)
    • Kernel Density Estimation (2)
    • Modified KDE, Nadaraya–Watson kernel regression
    • Kriging
    • Inverse distance weighting
    • Implementierung in Python
    • Existierende Implementierungen
    • Privatsphäre
  • Implementierung
    • Warum WebGL
    • Vergleich von Leaflet-GL Libraries
    • Alternativen: OpenLayers 3, Tangram
    • Leaflet Heatmap
    • kurze OpenGL einführung
    • Erklärung von Shader
    • Partitioning (Probably includes rewriting the leaflet gl library)

Actually Useful Shit

Existing Implementations

Surfer
gstat

KDE

https://en.wikipedia.org/wiki/Kernel_density_estimation
https://projecteuclid.org/journalArticle/Download?urlId=10.1214%2Faoms%2F1177704472
https://academic.oup.com/jrsssb/article/53/3/683/7028194?login=false
https://archive.org/details/densityestimatio00silv_0/page/46/mode/2up
https://espace.library.uq.edu.au/view/UQ:120006
https://the.datastory.guide/hc/en-us/articles/4602776994575-Effective-Sample-Size
https://docs.scipy.org/doc/scipy-1.11.4/reference/generated/scipy.stats.gaussian_kde.html?utm_source=chatgpt.com#ra3a8695506c7-1
https://arxiv.org/pdf/0709.1616v2
https://ia902902.us.archive.org/10/items/in.ernet.dli.2015.214343/2015.214343.Survey-Sampling.pdf
https://www.ajs.or.at/index.php/ajs/article/view/vol33%2C%20no3%20-%201