feomike

a core issue in implementing good policy, is a sound understanding of the problem to be solved. let me unpack that a bit. policy is good if it can solve a problem; it needs to address some issue which reasonable people collectively agree is worth solving. for exampe, lets say, building code is in place to ensure fire safety standards or to withstand environmental tragedy (say earthquake) or similar. most people would agree saving lives due to non-standard building implementations is a good thing. so in order to attack that problem, groups of people (non-profit/industry groups, industry, government, academics, etc) advocate, strategize, architect, pass and implement policy (standards, law, regulation, executive order etc) which solve the problems around standarization of building codes.

good policy stands the test of time, is robust to challenge, moves with opinion (popular, scientific and/or otherwise) and is high on the adoption curve; which is to say people generally want to adopt it. so for the sa

jwass

Thinking about the Turf tornado analysis from https://www.mapbox.com/blog/60-years-of-tornadoes-with-turf/ and what the similar approacoh is in GeoPandas.

The two programs take slightly different approaches to the counting. Turf loops over the counties, counting how many tornadoes fall inside its borders. GeoPandas performs a spatial join - first forming a spatial index on the tornadoes. The joined GeoDataFrame combines the columns (properties) of both sets. Then a groupby operation is performed counting the number of entries for each county.

The Turf version is significantly faster - most likely due to the slow spatial join operation in GeoPandas and that all columns are included resulting in a large final DataFrame - and probably just that node is much faster than Python here. Overall, Turf's speed is impressive.

sgillies

Star this Gist to indicate preference for the deeper form (with "when" and "@type") of GeoJSON-LD Time (geojson/geojson-ld#9).

jseppi

tmcw

This is a non-technical reading list for technical people.

• How to Program Independent Games
• Kill Math (and Don't Kill Math)
• Move Fast and Don't Break Things
• Launch & The Web Is a Customer Service Medium by Paul Ford
• _why at Art & Code
• Repetitive Process

This is a list of software you should read like a novel.

steko

Two alternative ways.

Using categories and custom sortkeys

A possible elegant way to achieve this objective.

Starting point is here:

http://www.mediawiki.org/wiki/API:Categorymembers

tmcw

As an ex-University lecturer I could do this in class and students would enter the workforce able to make their analysis look good on a map and communicate properly… What about those who can't be bothered reading a few pages from a book or a web site that shows them some useful tips?

You understand the academic mindset, not the hacking mindset. Academics go like this:

lectures -> books -> grades -> make a good map

Hacking goes like this:

make a map -> websites -> make a better map -> make a good map

jboner

Latency Comparison Numbers (~2012)
----------------------------------
L1 cache reference                           0.5 ns
Branch mispredict                            5   ns
L2 cache reference                           7   ns                      14x L1 cache
Mutex lock/unlock                           25   ns
Main memory reference                      100   ns                      20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy             3,000   ns        3 us
Send 1K bytes over 1 Gbps network       10,000   ns       10 us
Read 4K randomly from SSD*             150,000   ns      150 us          ~1GB/sec SSD

sgillies

Author: Sean Gillies Version: 1.0

Abstract

This document describes a GeoJSON-like protocol for geo-spatial (GIS) vector data.

Introduction

mweisman

# pipelinedemo.py
# Data processing pipeline demo
# Uses GeoJSON like geometries for demonstration only

from fiona import workspace
from shapely.geometry import box
from shapely import wkb
from json import dumps
import urllib2
import logging