Keith Jenkins kgjenkins

## hash.py
# this script goes through a list of files from Excel "data from folder"
# and computes a hash of the file contents (to be used for deduplication)

import csv
import os
import hashlib
import time

# columns: Name,Extension,Date accessed,Date modified,Date created,Folder Path,Hash

## readme.md

      
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                Creating halftones in QGIS
              
          
    Creating halftones in QGIS

A quick demo using a hexagon grid with a centroid fill of points sized using raster values from the image layer.


## README.md

      
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                kgjenkins
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              Created
              April 19, 2024 16:45
            
              
                variation across multiple scans
              
          
    The same image (an illustrated book cover) was scanned on the flatbed scanner (EPSON WF-2630) three times at 300dpi.
Analysis of the variation across the three scans:


band
min
max
mean
std dev


red
0
36
4.47
2.59


green
0
36
4.41
2.68


blue
0
37
4.72
2.95


## index.py
# this script goes through a directory of tif files and a shapefile of cities
# and reports which city points are contained within each tif

import os
import fiona
import rasterio
import shapely
from pyproj import Transformer

tifpath = "path/to/tiffs"

## gist:ad26a278ecbf745873b8504dd7e217b3
The limiting factor in terms of the speed of the Iso-areas tool seems
to be the number of starting points.  I've tried a number of
variations, trying to reduce the number of points while retaining
enough points that the analysis is still accurate.  After several
failed attempts, I think I've come up with a workflow that ends up
keeping just the points along the park perimeters that end up being
the closest points to the road network.  Here's the general workflow,
assuming we are starting with 2 layers, "streets" and "parks".  I'd
suggest creating temporary outputs for each of these steps, and then
make the final result permanent.

## readme.md

      
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                TIFF compression options
              
          
    TIFF compression options

Summary

Realistically, especially when considering the inherent noise in the original image, I'd settle for lossy compression with COMPRESS=JPEG JPEG_QUALITY=90, which could reduce file size fairly quickly to 16% of the original.
But if lossless compression is a hard requirement, I'd probably go with COMPRESS=LZW PREDICTOR=2.  However, I would want to verify that any downstream tools would support this sort of compression.
UPDATE: When saving with JPEG compression, further speed and size improvements are gained by adding PHOTOMETRIC=YCBCR, which uses a different color space that has even better compression. I've added new rows to the table for YCBCR, as well as .jp2 formats.

  
## index.html
<html>
<head>
<title>Cornell Theses and Dissertations by Academic Discipline</title>
<meta http-equiv='Content-type' content='text/html; charset=UTF-8'>
<meta name='author' content='Keith Jenkins'>
<meta name='dateModified' content='2022-01-11'>
<style>
body { font-family: sans-serif }
h1 { text-align:center ; background: #b31b1b ; color: #fff ; padding: 0.5em ; margin: 0 }
td { white-space: nowrap }

## README.md

      
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              Last active
              September 12, 2023 20:45
            
              
                Reclassifying UrbanWatch rasters using QGIS and Rasterio
              
          
    Reclassifying UrbanWatch rasters using QGIS and Rasterio

See the video demo at https://youtu.be/t-pb5-1Q_Qg
The files here were used to convert UrbanWatch from RGB images to numeric rasters with simple landcover values 0-9, as follows:


Value
Category
Color


0
No Data
Black


1
Building
Red


## README.md

      
              3 files
            
          
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              September 12, 2023 20:32
            
              
                Reclassifying UrbanWatch rasters using ArcGIS
              
          
    Reclassifying UrbanWatch rasters using ArcGIS

See the video demo at https://youtu.be/mdYnBFYqXpg
The files here can be used to convert UrbanWatch rasters from RGB images to numeric rasters with simple landcover values 0-9, as follows:


Value
Category
Color


0
No Data
Black


1
Building
Red


## readme.md

      
              1 file
            
          
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              February 21, 2023 18:53
            
              
                Interpolate points along lines using QGIS virtual layers
              
          
    Interpolate points along lines using QGIS virtual layers

Suppose we want to interpolate a number of points along each line in a layer:

We have a column called stations that specifies how many points to
interpolate on each line:
	# this script goes through a list of files from Excel "data from folder"
	# and computes a hash of the file contents (to be used for deduplication)

	import csv
	import os
	import hashlib
	import time

	# columns: Name,Extension,Date accessed,Date modified,Date created,Folder Path,Hash
	# this script goes through a directory of tif files and a shapefile of cities
	# and reports which city points are contained within each tif

	import os
	import fiona
	import rasterio
	import shapely
	from pyproj import Transformer

	tifpath = "path/to/tiffs"
	The limiting factor in terms of the speed of the Iso-areas tool seems
	to be the number of starting points. I've tried a number of
	variations, trying to reduce the number of points while retaining
	enough points that the analysis is still accurate. After several
	failed attempts, I think I've come up with a workflow that ends up
	keeping just the points along the park perimeters that end up being
	the closest points to the road network. Here's the general workflow,
	assuming we are starting with 2 layers, "streets" and "parks". I'd
	suggest creating temporary outputs for each of these steps, and then
	make the final result permanent.
	<html>
	<head>
	<title>Cornell Theses and Dissertations by Academic Discipline</title>
	<meta http-equiv='Content-type' content='text/html; charset=UTF-8'>
	<meta name='author' content='Keith Jenkins'>
	<meta name='dateModified' content='2022-01-11'>
	<style>
	body { font-family: sans-serif }
	h1 { text-align:center ; background: #b31b1b ; color: #fff ; padding: 0.5em ; margin: 0 }
	td { white-space: nowrap }