Is there a best practice in QGIS for summarizing point data in small cells over a large area?
Background/Problem: In QGIS, it is "possible" to create a grid of hex cells over an area using the extent of a layer. Then one can summarize the points which fall in that polygon grid. However this is very computationally wasteful to construct for points that cover a large extent, but which need to be analyzed in relatively small cells. For example, if points that cover an entire region need to be binned at a 100-500M resolution - this will leave the user waiting for hours while QGIS constructs the grid, then a while longer for it to do the summary stats.
In ArcGIS Pro, I can [mis]use the Optimized Outlier Analysis tool, which allows me to determine the grid type, size and distance band, ignoring other stats and returning only polygons drawn and populated with the counts of the points. This gives me the same sort of output (summarized hex cells) but at a tiny fraction of the time.
Is there a workflow/plugin in QGIS that will get me to a similar output?
I have tried the Hotspot Analysis plugin in QGIS, but A.) it appears abandoned and B.) it requires statistics fields as forced, not optional, and C.) pysal is tricky to get working correctly with QGIS on Windows.
UPDATE: Description of the Outlier Analysis Tool as requested, from the documentation:
Optimized Outlier Analysis executes the Cluster and Outlier Analysis (Anselin Local Moran's I) tool using parameters derived from characteristics of your input data. Similar to the way that the automatic setting on a digital camera will use lighting and subject versus ground readings to determine an appropriate aperture, shutter speed, and focus, the Optimized Outlier Analysis tool interrogates your data to obtain the settings that will yield optimal analysis results. If, for example, the Input Features dataset contains incident point data, the tool will aggregate the incidents into weighted features. Using the distribution of the weighted features, the tool will identify an appropriate scale of analysis. The classification type reported in the Output Features will be automatically adjusted for multiple testing and spatial dependence using the False Discovery Rate (FDR) correction method.