Análisis de Datos

Population lines, idea de los gradientes de Dani → http://spatial.ly/2017/04/population-lines-how-and-why-i-created-it/ https://www.r-bloggers.com/joy-division-population-surfaces-and-pioneering-electronic-cartography/

joyplot with d3 https://beta.observablehq.com/@mbostock/d3-ridgeline-plot
horizon chart with d3 https://beta.observablehq.com/@mbostock/d3-horizon-chart

When projections showed Hurricane Harvey could bring a record setting amount of rain to Houston, the graphics desk at the New York Times started exploring ways of showing the rainfall. After a couple of days of scrambling, we managed to make a map showing both the accumulation and rate of rainfall: → https://roadtolarissa.com/hurricane/

Use a data analytics tool to predict where the Pokemon are going to appear → https://www.pluralsight.com/guides/big-data/use-a-data-analytics-tool-to-predict-where-the-pokemon-are-going-to-appear

a visualization of global weather conditions forecast by supercomputers updated every three ours → https://earth.nullschool.net/about.html https://github.com/cambecc/earth http://www.copernicus.eu/

Using machine learning to identify air pollution exposure profiles associated with early cognitive skills among U.S. children → http://www.sciencedirect.com/science/article/pii/S0269749117313027

This playground exists to showcase a type of Swarm Intelligence known as Flocking. → https://tech.io/playgrounds/1003/flocking-autonomous-agents/going-further

JSGeo2017 → https://www.dropbox.com/s/xak1kodwly80z8z/JSGeo2017.pdf?dl=0

R package to analyze air quality data → https://www.r-bloggers.com/rtimicropem-using-an-r-package-as-platform-for-harmonized-cleaning-of-data-from-rti-micropem-air-quality-sensors/ https://ropensci.org/blog/blog/2017/02/21/ropenaq https://www.r-bloggers.com/working-with-air-quality-and-meteorological-data-exercises-part-2/ https://www.r-bloggers.com/working-with-air-quality-and-meteorological-data-exercises-part-4/

Grupo en valencia, españa → http://safe-tools.dsic.upv.es/airvlc/data-analysis.php https://valenciarusers.files.wordpress.com/2016/04/prediccic3b3n_contaminacic3b3n-lidiacontreras.pdf Los datos que tenemos pueden ser muy similares a estos que se obtienen a partir de strava → https://github.com/marcusvolz/strava

Fondo para una serie de tiempo con escala ICA https://media.licdn.com/mpr/mpr/jc/AAEAAQAAAAAAAAX4AAAAJDIxYTFjMzEzLWUzNzEtNDgzOS1iZDVlLTY4OTgzZTBmZWQxYw.png

Mapa 3d para visualizar mediciones http://www.seattlebikeblog.com/wp-content/uploads/2014/03/SBB_pollution_map-575x471.png origen de este mapa https://www.researchgate.net/publication/252731674_Exposure_of_Bicyclists_to_Air_Pollution_in_Seattle_Washington_Hybrid_Analysis_Using_Personal_Monitoring_and_Land_Use_Regression Podría implementarse con : http://rgraphgallery.blogspot.com.co/2013/05/rg107-plot-3d-horizontal-lines-bars.html

Artículo CSWA: Aggregation-Free Spatial-Temporal Community Sensing → https://arxiv.org/pdf/1711.05712v1.pdf

Viento y PM25 → http://aqicn.org/faq/2015-11-05/a-visual-study-of-wind-impact-on-pm25-concentration/

Predicción de concentración PM25 usando redes neuronales https://www.sciencedirect.com/science/article/pii/S1352231015001491

Anomaly detection with R → https://www.r-bloggers.com/anomaly-detection-in-r-2/

Animación de la contaminación en china → https://old.reddit.com/r/dataisbeautiful/comments/8v0m98/oc_3d_animation_of_chinas_nitrogen_dioxide/

Predicting Sunspot Frequency with Keras LSTM In R → https://www.r-bloggers.com/time-series-deep-learning-part-2-predicting-sunspot-frequency-with-keras-lstm-in-r/

Metaballs → http://jamie-wong.com/2014/08/19/metaballs-and-marching-squares/

AgentMaps - Social Simulations on Interactive Maps → https://noncomputable.github.io/AgentMaps/