You might need to carry out some sort of spatial interpolation which involves converting point data (attributes) into surface maps (rasters) or even contour maps... Read up more on "conversion of point data into surface maps on ArcGIS or QGis".. Some popular spatial interpolation methods include kriging, spline, IDW etc...

I use python to do that.

You will have to have your data points in CSV format (UTF8, comma separated, with headers as column names).

Then, you import it in Pandas (or GeoPandas, in my case) as a dataframe (or geodataframe) , and with Seaborn (plotting package, you need to import it), you generate heat maps of whatever you want.

All you need is a few lines of code.

See what I do with points extracted from transects:

input_table= gpd.read_file(r"C:\jupyter\tables\postgres_output\pfa_mean_tr_unpiv.csv")

To store your CSV into a geodataframe

input_table_piv=input_table.pivot('time_step','tr_id','mean_diff')

Pivot the table so that y-axis (index) is time_step, x-axis (columns) is tr_id and mean_diff is the data to color the heatmap

f, ax = plt.subplots(figsize=(20, 12))

To store figure and axis information in two variables and set the figure size to be plotted

sb.heatmap(input_table_piv, annot=False, center=0, linewidths=.5, ax=ax, cmap="RdBu");

To plot the data as a heatmap with divergent colour ramp RdBu (matplotlib library) centered at 0.

The image I created shows the elevation differences of points sampled every 1m along shore-normal elevation profiles extracted from coastal UAV-based digital surface models. Greyed-out points are outliers as computed below/above the 5th and 95% quantiles.

Too easy!

It partly depends on the nature of what your data points show concentrations of - and if the data points are evenly distributed over your study area in a spatial grid format. If not interpolation will be needed to show a representative 'contour' map/surface. Typically points where data has been collected are not evenly distributed. IDW works well with concentrations of parameters relating to the atmosphere but even then physical barriers on the ground (mountains/water bodies etc) can prevent the surface created from being representative. The data will need to be in shapefile or CVS format. I'm not a QGIS user but the atteched workflow looks good and has some output images so you can see if that's exactly what you're hoping to achieve: https://gracilis.carleton.ca/CUOSGwiki/index.php/Creating_IDW_and_Spline_Interpolation_Maps_Using_QGIS

Thank you all so much for your time and helpful replies. I highly appreciate it.

Nicolas Pucino Great idea! Thank you!

At the moment I'm working with geoSpatial data, that's why I want to create a real map (neither a chart nor table). But I will definitely try to do the same as you suggested for another part of the research. Interesting way to illustrate the result!

Thank you so much.

https://www.reneshbedre.com/blog/heatmap-python.html

https://biit.cs.ut.ee/clustvis/

http://www.heatmapper.ca/

https://www.biostars.org/p/205417/