sgillies

Author: Sean Gillies Version: 1.0

Abstract

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

Introduction

catawbasam

# -*- coding: utf-8 -*-
"""
LICENSE: BSD (same as pandas)
example use of pandas with oracle mysql postgresql sqlite
    - updated 9/18/2012 with better column name handling; couple of bug fixes.
    - used ~20 times for various ETL jobs.  Mostly MySQL, but some Oracle. 

    to do:  
            save/restore index (how to check table existence? just do select count(*)?), 
            finish odbc, 

fge

{
    "$schema": "http://json-schema.org/draft-04/schema#",
    "id": "http://json-schema.org/geojson/geometry.json#",
    "title": "geometry",
    "description": "One geometry as defined by GeoJSON",
    "type": "object",
    "required": [ "type", "coordinates" ],
    "oneOf": [
        {
            "title": "Point",

nicerobot

#!/bin/bash

# Run this:
#
# curl https://gist.github.com/nicerobot/7664605/raw/install.sh | bash -s do-sudo
#
# which will run:

[ -f qgis2-homebrew-build.sh ] || {
	curl -O https://gist.github.com/nicerobot/7664605/raw/qgis2-homebrew-build.sh

jsmits

<toolSet name="Code Checking">
  <tool name="Flake8" showInMainMenu="true" showInEditor="true" showInProject="true" showInSearchPopup="true" disabled="false" useConsole="true" showConsoleOnStdOut="false" showConsoleOnStdErr="false" synchronizeAfterRun="true">
    <exec>
      <option name="COMMAND" value="/usr/local/bin/flake8" />
      <option name="PARAMETERS" value="--max-complexity 10 $FilePath$" />
      <option name="WORKING_DIRECTORY" value="$ProjectFileDir$" />
    </exec>
    <filter>
      <option name="NAME" value="Filter 1" />
      <option name="DESCRIPTION" />

jepio

#!/usr/bin/env python3
'''
Filter to wrap Pandoc's CodeBlocks into minted blocks when using latex.
Pandoc's `fence_code_attributes` can be used to provide:

- the language (first class)
- minted's argumentless options (following classes)
- minted's options with arguments (attributes)
'''

tmcw

Geospatial privacy initial cheatsheet

There's more to it than this but this is a decent starting point.

1. If there are personal identifiers in the dataset, the safest approach is to remove them entirely. If you need to keep them in to enable analytics on an 'anonymous user over time', make sure that you don't use a reversible technique like MD5 hashing. See the NYC Taxicab debacle for an example. 2. If you use hashes, use a long salt value and a cryptographically okay hash like SHA512. 3. Or randomize data order and assign serial (increasing integer) numbers to identifiers. 4. But really just removing any kind of identifiable ID is better than trying to obscure it.
2. Set lower bounds for aggregation: if someone filters an API down to a single record, you may want to return nothing. There's the case where someone's able to craft a well-filtered query and just see one user’s data. See