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I have a dataset of georeferenced flickr posts (ca. 35k, picture below) and I have an unrelated dataset of georeferenced polygons (ca. 40k, picture below), both are currently panda dataframes. The polygons do not cover the entire area where flickr posts are possible. I am having trouble understanding how to sort many different points in many different polygons (or check if they are close). In the end I want a map with the points from the flickerdata in polygons colord to an attribute (Tag). I am trying to do this in Python. Do you have any ideas or recommendations?

Point dataframe Polygon dataframe

diana
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1 Answers1

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Since, you don't have any sample data to load and play with, my answer will be descriptive in nature, trying to explain some possible strategies to approach the problem you are trying to solve.

I assume that:

these polygons are probably some addresses and you essentially want to place the geolocated flickr posts to the nearest best-match among the polygons.

First of all, you need to identify or acquire information on the precision of those flickr geolocations. How off could they possibly be because of numerous sources of errors (the reason behind those errors is not your concern, but the amount of error is). This will give you an idea of a circle of confusion (2D) or more likely a sphere of confusion (3D). Why 3D? Well, you might have flickr post from a certain elevation on a high-rise apartment, and so, (x: latitude,y: longitude, z: altitude) all may be necessary to consider. But, you have to study the data and any other information available to you to determine the best option here (2D/3D space-of-confusion).

Once you have figured out the type of ND-space-of-confusion, you will need a distance metric (typically just a distance between two points) -- call this sigma. Just to be on the safe side, find all the addresses (geopolygons) within a radius of 1 sigma and additionally within 2 sigma -- these are your possible set of target addresses. For each of these addresses have a variable that calculates its distances of its centroid, and the four corners of its rectangular outer bounding box from the flickr geolocations.

You will then want to rank these addresses for each flickr geolocation, based on their distances for all the five points. You will need a way of identifying a flickr point that is far from a big building's center (distance from centroid could be way more than distance from the corners) but closer to it's edges vs. a different property with smaller area-footprint.

For each flickr point, thus you would have multiple predictions with different probabilities (convert the distance metric based scores into probabilities) using the distances, on which polygon they belong to.

Thus, if you choose any flickr location, you should be able to show top-k geopolygons that flickr location could belong to (with probabilities).

For visualizations, I would suggest you to use holoviews with datashader as that should be able to take care of curse of dimension in your data. Also, please take a look at leafmap (or, geemap).

References

CypherX
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