OpenStreetMap
There’s a guy in Amarillo, Texas who is dead set on vandalizing the same features over and over and over. He’s been banned at least 4 times now. This diary entry will be used to keep track of all the user’s alt accounts and edits.
Usernames
Popular features to vandalize
A geoglyph of a seal 75m x 35m drawn to appear to swim east to west on moderately sloped land. It is unclear where the seal is going however its proximity is rather close to another ocean related geoglyph. There seems to be are other shapes in the immediate area that suggest this is a scene related to an ocean.
15°17′33″S, 74°32′43″W -15.2923897, -74.5454017 Acarí, Province of Caravelí, Arequipa, Peru
A monolithic 20m x 25m split rock and trail going through it might have been some kind of benchmark. Located on moderately sloped land and appears split right down the middle.
15°17′32″S, 74°32′33″W -15.2923595, -74.5425975 Acarí, Province of Caravelí, Arequipa, Peru
Two geoglyph heads appear to be stacked one on top of the other on moderately sloped land. The bottom head is 25m x 25m and the top head significantly smaller 5m x 5m, oriented north to south. Both heads seem to suggest having bodies but they are hard to read at this time. Both heads look in the forward direction. It is unclear why they were created or what they are doing there.
The bottom head suggests that its body seems to have a bent arm, torso and two legs, though very short and stubby, they do appear there. The upper head, being smaller seems to have a smaller body and is unclear if the head is resting on a hat the of the bottom head. It might be suggested the top head is of a monkey.
15°17′27″S, 74°32′23″W -15.2907672, -74.5397234 Acarí, Province of Caravelí, Arequipa, Peru
Gallant
Recent articles and blog posts about paid editing in OSM has renewed interest in the topic on social media and OSM discussion channels. The data and numbers presented in these discussions primarily come from a paper I co-authored in 2019, and are now outdated. This diary post presents new, updated figures.
Paid editing in OSM is receiving new attention in light of two articles in the past few months that are reporting on the phenomenon. Both articles heavily cite numbers from our 2019 Corporate Editing in the Evolving Landscape of OpenStreetMap paper:
These articles have prompted some discussion on Twitter from the larger OSM Community. What’s missing in these follow-up threads, however, are updated figures regarding the editing over the past two years.
This post only presents updated figures relevant to paid-editing in OSM and observational analysis. As the OSM research community continues to expand, stay tuned for more in-depth research in this space, such as: novel ways to identify undisclosed paid-editors and occupational mappers, new community-detection algorithms from editing editing patterns, and further investigations of the mapping interactions between paid and unpaid editors. At the end of this diary post I include a glossary with some of the terms in both this and previous posts of mine such as paid editing, professional editing, and occupational editing. These terms are becoming more common in this research space, so I am hoping to better introduce and define them.
I liked Pete’s diary at the first sight. However, when published, I was already decided about my next work. Last Thursday I finally found some time to look closer at this interesting topic. I spent night working on it. And I will share the results in this diary.
First, I will not write about the HOT tasking manager. Second, these are my opinions. Third, understand damn in the following text as the shortcut of Divide and map. Now.
The summary of my understanding of the original diary follows. I’ll append my thoughts, though. I made a few toots during the night, which can serve to track the time effort.
Questions:
Problems:
Consequences:
Thoughts:
The damn project has advantage here, because the name says exactly what the project does. Using areas, squares, and commits notation improves the clearness, too.
As there are multiple clients, each of them targets different group of mappers. Therefore, it’s easier to improve client for newbie mappers without limiting the experienced ones.
Each commit to the area is stored and shown when requested (statistics page,) improving the understanding of the area history.
Original problem:
Solution:
Onboarding process. Making sure that managers fulfill the expectations of having skills to:
Im growing quite fond of editing. Loving it, i see great things coming from this.
ชานมไต้หวัน ราคาเริ่มต้นที่ 19 บาท
A 20m x 40m geoglyph of a fish is oriented east to west. Etched on moderately sloped rock in profile and shown with a single dark visible eye, body fins and gills. The fish appears to be one of several hard to read geoglyphs nearby..
14°52′19″S, 74°56′26″W -14.8719975, -74.9405325 Vista Alegre, Province of Nasca, Ica, Peru
A geoglyph of a laughing face (25m x 25m) oriented north to south on a moderately sloped rock is a little hard to see. A front view image with both eyes appears to roll to the left, a long nose and gaping large mouth appears as its laughing. There is possibility of this geoglyph also has a neck and shoulders and torso. It is unclear what the geoglyph seems to be laughing at and why, the face is located to the right (east) of another geoglyph a sea lion. The reason for the laugher might simply be the joy in seeing these amazing animals.
14°52′21″S, 74°56′8″W -14.8725180, -74.9356717 Vista Alegre, Province of Nasca, Ica, Peru
Frog Park
Mit dem Werkzeug osmium-tool kann man OSM-Extrakte nach Schlüssel/Wert-Kombinationen filtern. Das funktioniert auch mit History-Dateien. Dabei werden alle Objekte herausgefiltert, bei denen es einen Version mit der gesuchten Schlüssel-Wert-Kombination gibt. Auf diese Weise ist es z.B. möglich, alle Objekte zu extrahieren, bei denen der Wert eines Schlüssel in der Versionshistorie des Objekts in einer bestimmten Weise geändert wurde, z.B. von highway=track auf highway=service|unclassified|tertiary:
# Create data file from history file to ensure consistency between data and history files:
osmium time-filter extract.osh.pbf -o extract.osm.pbf
# Get all ways with highway=track in any version
# (Discard nodes in the first step in order to eliminate nodes that are no longer part of the current versions of the ways)
osmium tags-filter --omit-referenced extract.osh.pbf w/highway=track -o track-w.osm.pbf
osmium getid --id-osm-file track-w.osm.pbf --add-referenced extract.osm.pbf -o track.osm.pbf
# Keep only ways that are now service,unclassified or tertiary:
osmium tags-filter track.osm.pbf w/highway=service,unclassified,tertiary -o no-track.osm
Die Datei no-track.osm kann mit JOSM geöffnet werden.
Manchmal ist es hilfreich, Objekte zu extrahieren, an denen der ursprüngliche oder der aktuelle Wert von einem bestimmten User gesetzt wurde. Mit osmium ist das leider nicht möglich, da nicht nach den Schlüsseln user und uid gefiltert werden kann.osmium kann aber die History-Datei in eine OPL-Datei umwandeln, ein Textformat mit einer Zeile pro Objektversion, in der auch der Username enthalten ist. Eine andere Möglichkeit bietet das Tool
osm-tag-csv-history. Beide Wege werden nachfolgend beschrieben. In beiden Fällen wird zunächst eine History-Datei der bisher herausgefilterten Objekte erstellt:
osmium getid --id-osm-file no-track.osm --with-history extract.osh.pbf -o no-track.osh.pbf
Se preferirem trabalhar só com os pontos de entrega do correio, em vez de terem o mesmo ponto repetido para cada endereço, como acontece nos prédios, criei uma tabela entregas.
A tabela entregas mantém toda a informação, pois estou a agregar a informação das diferentes moradas num único ponto.
Criei uma pasta Pontos de Entrega Continente onde têm uma exportação por concelho.
Para quem usar o QGIS, criei nessa pasta um geopackage continente.gpkg que tem as duas tabelas: moradase entregas.
Também fiz um dump do Postgresql, que está nessa pasta em enderecos-202103291045.backup.
Atenção: as shapefiles têm limitações no comprimento dos atributos. Algumas agregações estão truncadas (pelo limite de 255 carateres do formato shapefile.
A geoglyph of a puffer fish (25m wide by 50m long) oriented north (head) to south (tail) on relatively flat has both eyes and a wide open mouth. The geoglyph includes a tail fin and two dorsal fins. The shape of the fish is large in the state that it isn’t puffed up. It is unclear how the fish is associated to the immediate surroundings. There seems to some other very nearby geoglyphs but possibly highly damaged and hard to read.
14°48′29″S, 74°55′24″W -14.8081793, -74.9233388 Nazca, Province of Nasca, Ica, 11401, Peru
Para sistematizar a importação do continente, podem-se usar as seguintes scripts:
Sacar índices, gerar uma script para sacar os zips e sacar os zips:
#!/bin/bash
wget https://inspire.ine.pt/AD/atom/CDG_AD_BNM_pc_Norte_Atom.xml
wget https://inspire.ine.pt/AD/atom/CDG_AD_BNM_pc_Centro_Atom.xml
wget https://inspire.ine.pt/AD/atom/CDG_AD_BNM_pc_AML_Atom.xml
wget https://inspire.ine.pt/AD/atom/CDG_AD_BNM_pc_Alentejo_Atom.xml
wget https://inspire.ine.pt/AD/atom/CDG_AD_BNM_pc_Algarve_Atom.xml
awk '/Addresses_[0-9]+_EPSG3763.zip/ {print $3}' *.xml | sed 's/href=/wget /' > saca.sh
mkdir zips; cd zips; bash ../saca.sh
A script cria uma pasta zips com 278 zips (um para cada concelho do continente).
Para o restante processamento, usa-se o GDAL e uma base de dados PostgreSQL, onde é criada uma tabela moradas.
Nessa pasta, corre-se a seguinte script que processa concelho a concelho. Para cada concelho, são acrescentados os respetivos endereços à tabela moradas.
Script processa.sh:
#!/bin/bash
for filename in *.zip
do
base="${filename%.*}"
unzip $filename
echo "${filename%.*}"
GML_ATTRIBUTES_TO_OGR_FIELDS=YES GML_SKIP_RESOLVE_ELEMS=NONE ogr2ogr -f PostgreSQL PG:'service=osm' GMLAS:$base.gml -nlt CONVERT_TO_LINEAR
psql service=osm -f insere.sql
rm $base.gml
done
A script processa.sh recorre a uma script SQL insere.sql, que é a seguinte:
Well, after a lot of field activity, introducing OpenStreetMap to Ugandan rural, humanitarian and govenmental communities in Uganda, Sierra Leone, Zimbabwe, and through conference presentations, Namibia and Tanzania, I finally decided to write a chapter about ‘Motorcycle Mapping’ - a concept and practice developed by Ivan Gayton (and myself) in West Africa, and scaled into Uganda under my watch as Country Manager.
The book in which this chapter appears was expected to attract a relatively niche market, until COVID happened globally, and a newly-found interest in Data Ethics was born out of various scandals around government treatment of COVID data in the UK.
Free downloads of the book are available: https://www.sas.ac.uk/publications/mapping-crisis, and an abstract for my chapter can be found here: https://rupertallan.com/modalities-of-united-statelessness-chapter-9-abstract/
For nearly a year, the government outbreak effort extensively overlooked humanitarian knowledge and experience on how to manage COVID data in the UK, but it seems they are consulting Epidemiologists now (including MSF).
During the last few months, COVID figures have been making headlines in Merthyr Tydfil, South Wales, and OpenStreetMap now has an opportunity and support to map the South Wales Valleys. I feel fortunate to have lived and worked in the Valleys in the 1990s, and am excited to be part of this venture. At time of writing, there is every chance that Merthyr Tydfil will see african refugees working in its streets and alleys, helping its community to map their own vulnerability metrics.
Na importação das moradas do INE, estava a perder o “nome do edifício”, que é importante. Nalguns casos, os endereços não têm número, mas têm “nome do edifício”, como é ilustrado na imagem seguinte (em Beja).
Assim sendo, a query para passar das múltiplas tabelas criadas a partir do GML para uma única tabela passa a ser:
site com diversas ferramentas para uso no Openstreetmap. https://www.umbraosm.com.br/ferramentas-para-o-osm.html
*https://www.openstreetbrowser.org/
*https://taginfo.openstreetmap.org/
*https://maproulette.org/dashboard
Mais Ferramentas acesse o site; https://www.umbraosm.com.br/ferramentas-para-o-osm.html
Dados para consulta e Uso no OSM de Prefeituras e Governos estaduais e federal.Brasil https://www.umbraosm.com.br/2021/03/27/dados-para-consulta-e-uso-no-osm-de-prefeituras-e-governos-estaduais-e-federal/
Data for consultation and use in the OSM of city and state and federal governments. Brazil.
Lista de prefeituras que publicam dados potencialmente compatíveis com a ODbL.
Informe a URL e a licença/permissão de uso, quando possível. É sempre interessante colocar qualquer url ref. a um município, mesmo se os dados são privados (o mesmo link poderá apontar para dados abertos mais tarde), entretanto, antes de usar os dados para edição, verifique
Alagoas
Maceió http://www.sedet.maceio.al.gov.br/pdf/codigo_edificacoes/ e http://www.sedet.maceio.al.gov.br/pdf/plano_diretor/ Arquivos em PDF
Bahia
Feira de Santana http://geopolis.ba.gov.br/
Ceará
Fortaleza https://urbanismoemeioambiente.fortaleza.ce.gov.br/infocidade/498-cartografias Mapas de fundo são do Google
Distrito Federal No DF tem bastante dados aqui: https://www.geoportal.seduh.df.gov.br/.
Não sei exatamente a licença de uso atrelada. Espírito Santo
Cariacica http://www.cariacica.es.gov.br/prefeitura/secretarias/semdec/#tabitem_0_3 Serra http://drive.google.com/open?id=0ByzRD7X96jzScHJiNF9xaHpEcGM
Vitoria http://geoweb.vitoria.es.gov.br/#/downloads
Vila Velha http://www.vilavelha.es.gov.br/paginas/desenvolvimento-urbano-e-mobilidade-arquivos-de-georreferenciamento Linhares http://187.60.167.210/sig-linhares/ Verificar possibilidade de download
Governo Estadual
Órgão Status URL dos dados Licença/permissão Notas GEOBASES https://ide.geobases.es.gov.br/layers/?limit=20&offset=0&type__in=vector
