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Good morning. I have created a distance matrix in which there are distances between EU regions (NUTS2 level), considering some economic and demographic features. Then I have kept only distances from "Lombardia" (an Italian region) and i have ordered them in ascending order. I got the following data frame:
distance
Lombardia 0.0000000
Emilia-Romagna 0.5471997
Veneto 0.6692634
País Vasco 0.6757556
Piemonte 0.7419351
Toscana 0.7641841
Niedersachsen 0.7910501
Marche 0.8066858
Abruzzo 0.8587898
Hessen 0.8646235
Cataluña 0.8855014
I would like to get a good graphical visualization of this table, a visualization that explains well the distances between the regions. I thought that I could use geographical data (for example a Spatial Polygon DataFrame), in order to obtain a map of Europe divided into regions: I would color similar regions in the same way.
However, I don't know how to get a chart like this.
How can I visualize my data?
I usually use R, but I could use also Python.
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I am trying to create a machine learning algorithm which would help in categorizing the types(highway, city-road) of roads in a satellite image.
I've tried using image segmentation to extract the roads from satellite images, and thought of a process as follows:
Based on the color of the road, dirt roads can be predicted
Based on the thickness of the road, highways and city roads can be predicted
But the problem is I don't know how can differentiate the results based on the aforementioned criterion.
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I have a Set of lets say 100 points. And the distance of a point from any other point is given. Which means I have 100x100 dataset giving me distance of each of the 100 points from all the other 100 points. I want to form clusters from this dataset based on the condition that distance between any two points in a cluster should not be greater than x(where x can be for example 25kms.).
I am new to clustering and data science. Please guide me how to solve this problem. What libraries can most efficiently solve this problem. Any help will be appreciated. :)
This can be solved using sklearn's agglomerative clustering by setting the affinity as "precomputed"
Refer this link for the solution.
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I am trying to find the unique area from multiple bounded box generated to find the screen capture by the products in python
You can try threshold the image by colors first (using either HSV threshold, or RGB).
Then having several binary images, you can use Contour Approximation (number 4 on the page) feature, using Douglas-Peucker algorithm. Fill the resulting bounding boxes.
Afterwards, you can subtract resulting binary images from one another to find exact areas of intersections.
Hope it helps!
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I'm currently analyzing air quality data. I've got long arrays of 17 columns and around 100 rows/day that I'll be averaging. I'd like to plot some of that data, specifically Ozone concentrations, on a map.
Basically, my visualized final product is this:
So, where the air quality sensor is, I want to highlight a 1x1 km area around it in a specific color corresponding to the value of ozone concentration the sensor is reading (either over an average day or at a specific time).
Is there any python module that could help me achieve this product/plot my data onto a map well?
As "Basemap" is a rather popular package for creating maps in Python, I suggest having a look at this article: How to draw rectangles on a Basemap
Once you figure out how to draw rectangles that are scaled to your specifications, it should be easy to fill them with a color representing your data.
Cheers!
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I want to make a 2-D map and I have a set of data with X and Y as their coordinates and each point has a probability distribution based on one of its properties.
In somehow I would like to smooth this probability distribution of each point in the x and y coordinate and make a 2-D map based on the maximum and the width of the peak of each point probability distribution.
how could I use python to implement it?
It looks like this question is going to be closed as it seems way too broad, but I think you are looking for Kernel Density Estimation, or for Multivariate kernel density estimation.
Loosely, each point defines a distribution (the kernel) which when convolved, gives you a "smoothed" version. See the linked article at the picture below for a better example:
Scipy has a Gaussian KDE.