Yuriy Czoli YKCzoli

Title	Author	Link
Chicken Hill	Joy Williams	link
The Balloon	Donald Barthelme	link
The President	Donald Barthelme	link
Game	Donald Barthelme	link
The Party	Donald Barthelme	link
The School	Donald Barthelme	link
The Great Hug	Donald Barthelme	link

Process

tl;dr

This is the scrappy technical overview of processing the Southern Ontario 1954 imagery. I assume you are familiar with gdal, bash scripting, raster imagery. If you are trying to process similar data and something is unclear, feel free to reach out to me with questions.

I first found the data here:

https://mdl.library.utoronto.ca/air-photos/1954

Converting shapefiles to geojson

####Assumptions

QGIS installed (if not: http://qgis.com/)

Get the data

We will use the Electoral District and Polling Division shapefiles found on the Elections Ontario site:

Elevation Data portals

The National Oceanic and Atmospheric Administration (NOAA). Great for coastal regions/cites. Includes lidar coverage in certain areas.
http://www.coast.noaa.gov/dataviewer/#app=b1ed&bda3-selectedIndex=1
U.S. Geological Survey (USGS). Their earth explorer has a wide variety of data sets including satellite imagery, satellite radar (SRTM), and pockets of lidar coverage.
- http://earthexplorer.usgs.gov/

Giving context to open spending data

Historic satellite imagery and flood response

Yesterday the government of Mexico launched datos.gob.mx to open up government data across all ministries. We [built a mapping tool](blog post) for ministries to quickly build rich maps from data on the site. The tool also makes it to combine government data with other open datasets. This provides context and meaning to complex government data.

The first dataset that we mapped was all 2013 funds for disaster response and reconstruction. The map plots thousands of reconstruction projects across 45 natural disasters, including Hurricane Manual and Ingrid which affected two-thirds of Mexico, killing 192 people and causing $75 billion pesos in damage.

This is an incredibily rich and complex dataset. But this data alone is not particularly helpful. We need to better understand the context to understand why government invested funds the w

Running LAStools on your Mac

If you want to use LAStools on OS X you will have to do some prep. This is a brief introduction to get you up and running with LAStools on a Mac in the Terminal.

You may have heard that you should use wine to run LAStools on OS X. Depending on your experience this might sound intuitive, or like utter jibberish. For those who empathize with the latter, let's walk through this.

Homebrew

	#!/usr/bin/env python

	# ndvi.py red.tif nir.tif output-ndvi.tif
	# Calculate NDVI (see Wikipedia). Assumes atmospheric correction.
	# (Although I use it without all the time for quick experiments.)

	import numpy as np
	from sys import argv
	from osgeo import gdal, gdalconst

	#!/bin/bash

	echo "Enter bands in descending order, (ie 4 3 2)"

	read B1 B2 B3

	echo "Enter the resulting filename"

	read outputname

	#!/bin/bash

	echo "Enter bands (ie 4 3 2)"

	read B1 B2 B3

	echo "Enter the resulting filename"

	read outputname

	<!DOCTYPE html>
	<meta charset="utf-8">
	<body>
	<script src="http://d3js.org/d3.v3.min.js"></script>
	<script src="http://d3js.org/topojson.v1.min.js"></script>
	<script>

	var width = 960,
	height = 720,
	origin = [71, -42],