Maning Sambale maning

Specs

A5 booklet type
Matte/glossy thick paper

Frontpage

Title: State of the Map Asia Conference x Asia Resilience Forum
Some full page graphic + Logo
Sponsors platinum (small)
Mapbox, Visions

NYC had an import of over 1 million building footprints and 900,000 addresses in 2014 from the New York City Department of Information Technology and Telecommunications (DoITT). The DoITT GIS releases an updated shapefile of the footprints every quarter, and the latest version can be accessed here: Building footprints | Address points

Open datasets like these are a great opportunity to explore how OSM can be used as a bridge between authoritative information and that crowdsourced by citizens. Two years after the import, it is interesting to see how the OSM data compares with the latest official footprints. The interesting questions to ask is:

What has improved in the DoITT footprints that can be updated in OSM?
What has

Vandalism Case in OpenStreetMap: bryanpiczon

The OSM-PH community have been monitoring this user for years. Since his previous edits were mostly in the Philippines, we are trying to resolve this within the local community. But lately, we noticed he has started editing in other parts of the world and on "highly valuable" OSM features. We feel we should escalate monitoring by sharing this to the larger OSM community.

OSM userid: 688302, 698612, 2041564, 3179745, 3318041, 3431335, 3611662

We think these are vandalism/harmful edit accounts of the same person (identfying as he based on username) for the following reasons:

My initial contact via OSM private message was ignored. We reported the user to DWG and was blocked.

Source

http://geohub.lacity.org/datasets/813fcefde1f64b209103107b26a8909f_0

SRS Info

cat Building_Footprints.prj 
PROJCS["NAD83_California_zone_5_ftUS",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137,298.257222101]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]],PROJECTION["Lambert_Conformal_Conic"],PARAMETER["standard_parallel_1",35.46666666666667],PARAMETER["standard_parallel_2",34.03333333333333],PARAMETER["latitude_of_origin",33.5],PARAMETER["central_meridian",-118],PARAMETER["false_easting",6561666.667],PARAMETER["false_northing",1640416.667],UNIT["Foot_US",0.30480060960121924]]

	#!/usr/bin/env python3

	'''
	pip install tqdm numpy pillow mercantile 'rasterio==1.0b1' 'rio-tiler==1.0a7'
	'''

	import os

	import argparse

	#!/bin/bash
	#
	# ogrcat
	# produces line-delimited geojson features
	# Like `fio cat` but uses ogr2ogr under the hood


	ECHO_FEATURES=""

	ogr2ogr -f GeoJSON /vsistdout/ "$@" \| while read line

	#! /bin/bash -ex

	# Convert Landsat 8 GeoTIFF images into RGB pan-sharpened JPEGs.
	#
	# Requirements:
	# * gdal http://www.mapbox.com/tilemill/docs/guides/gdal/
	# * Dan Stahlke's https://github.com/gina-alaska/dans-gdal-scripts
	# * convert (image-magick)
	#
	# Reference info:

	<!DOCTYPE html>
	<html>
	<head>
	<meta charset='utf-8' />
	<title></title>
	<meta name='viewport' content='initial-scale=1,maximum-scale=1,user-scalable=no' />
	<script src='https://api.tiles.mapbox.com/mapbox-gl-js/v0.26.0/mapbox-gl.js'></script>
	<link href='https://api.tiles.mapbox.com/mapbox-gl-js/v0.26.0/mapbox-gl.css' rel='stylesheet' />
	<style>
	body { margin:0; padding:0; }