Keith Jenkins kgjenkins

Different methods to calculate polygon neighbors in QGIS

kgjenkins, 2020-11-10

Before using any of the methods below, make sure that your polygon layer has a spatial index.
Reported timings are based on test data of 1606 counties in the eastern US.

Method 1: Virtual Layers SQL query

select
 a.GEOID,

Merge state-based PUMA files and calculate adjacent polygons

kgjenkins, 2020-06-26

Download PUMA boundaries

The US Census has PUMA boundary shapefiles available by state. Here's the directory of all the 2010 PUMAs, 2019 vintage: https://www2.census.gov/geo/tiger/TIGER2019/PUMA/

Download manually one at a time, or automate using wget:

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	max	mean	std dev
red	36	4.47	2.59
green	36	4.41	2.68
blue	37	4.72	2.95

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

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

LA 3010 GIS data sources

by Keith Jenkins, [email protected]
GIS Librarian at Mann Library, Cornell University
Fall 2022

New York City open data

https://opendata.cityofnewyork.us/

	# this script goes through a directory of tif files and a shapefile of cities
	# and reports which city points are contained within each tif
	# 2024-04-17 kgjenkins

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

	# 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.