Making Maps from OpenStreetMap Data: Unterschied zwischen den Versionen

Version vom 26. März 2014, 17:39 Uhr

See also OSM, OpenStreetMap-Daten zu Karten aufbereiten (german)

 >> Work in progress... <<

Overview

This is a how to/knowhow page about making own web maps from OpenStreetMap data.

• If you are looking for making nice own base (or background) maps with own styling for print or web eventually within own region, and without programming skills - then this is the place to look for. Examples of personalized base maps are blind maps (maps without labels), the watercolor map from Stamen, OpenCycleMap among others.
• If you are looking for making a thematic map on top of given base maps, use Maperitive or other services (see Geovisualisierung (german)). Examples of thematic maps is Wheelmap with infoboxes.
• This is still work in progress. Our plan (see Team below) is to put everything in a single script file (tbd.).
• See also Map Making Overview at OSM Wiki.

Use Case 'Vintage Map'

As a first use case we will show how to make a Vintage Maps from OpenStreetMap data.

tbd.

Your Use Case Here?

 If you have a nice map styling (preferrably in CartoCSS) 
 based on any 'map oriented' OSM database, 
 pls. contact us (see Team below)!

Cookbook

• Software: tbd.
• Data: tbd.
• Hardware: tbd.

See workflow.

Some background: Making maps actually means in this context transforming a dataset to graphics. Before being styled (or rendered) the dataset is being preprocessed which ultimately would be a one-click process - but it's not yet since technology and user demands are changing fast.

At least, we can make life easeier when the intermediate dataset structure is has some known rules:

table name schema "<prefix>_<name>_<postfix>":

• points
• lines
• polygons
• roads, roads_gen0, ...
• pois
• boundaries (boundaries_gen0, boundaries_gen1, ...)

Zoom levels:

• 0-10 => <name>_gen0
• 11-14 => <name>_gen1
• >= 12 => <name> benutzt

Workflow

Pls. note that this is still work in progress and thus experimental, suboptimal and at worst partially broken.

The workflow consists of followin steps:

• Step 0. Prepare software and config data.
• Step 1. Download, convert and clip OSM data. You can skip to step 2 and jump to step 3 "Style data" if you are in a hurry.
• Step 2. Enhance and preprocess SQLite dataset.
• Step 3. Style data.
• Step 4. Publish map.

As said before, our goal is to wrap step 1 and 2 into one single Python script.

Step 0. Prepare software and config data

• Install software: OGR/Python, SpatiaLite, TileMill
• Get config data, e.g. modified osmconf.ini
• Prepare clip boundary (e.g. liechtenstein_schloss_bbox.geojson)

Step 1. Download, convert and clip OSM data

• Open command line shell (PBF file in, SQLite/SpatiaLite file out):

 > ogr2ogr -f "SQLite" liechtenstein.sqlite
   /vsicurl/http://download.geofabrik.de/europe/liechtenstein-latest.osm.pbf 
   -dsco SPATIALITE=YES -skipfailures -progress -overwrite 
   --config OSM_CONFIG_FILE osmconf.ini -gt 65536 
   -clipsrc liechtenstein_schloss_bbox.geojson -clipsrclayer OGRGeoJSON 
   -nlt PROMOTE_TO_MULTI

Open points:

• Convert coordinates to Mercator?

Alternatives (not used further here; this does not mean they are unusable - in contrary! - but that the are just not considered for different reasons):

• QGIS - reads PDF/OSM data with OGR (like we do here) but with default config and without preprocessing.
• spatialite_osm_map (OSM import tool from Spatialite) - no config of own tag->attribute mapping, no clipping possible?
• osm2pgsql - requires PostGIS which is cumbersome being another local server software.
• osmosis - no tag->attribute mapping?

Step 2a. Enhance SQLite dataset

• Enhance dataset by shuffling geo objects around (using Spatialite, SQLite file in/out)
• Convert Polygons to Points and add them to points as POIs.
• Eventually convert multilinestring to linestring (can TileMill/Mapnik and QGIS handle multilinestrings/multipolygons?).

Step 2b. Do some other preprocessing

• use preprocessing tools (Python, SQLite file in/out)
• line smoothing of streets
• tbd.

Sources:

• http://www.imagico.de/map/water_generalize2_en.php

Ideas:

• DB related
  • Tippfehler in Tag-Values korrigieren
  • Tags vereinheitlichen
• Geometry related:
  • Generalisierung von Wald/Gewässer/Straßen

Step 3. Style data

• Start TileMill

Sources:

• CartoCSS and TileMill
• Ready made icons: Symbolkatalog

Step 4. Publish map

• On paper or on the web
• See e.g. TileMill or Geovisualisierung

Database Schema

Notes

See also Diskussion:Making Maps from OpenStreetMap Data.

Team

• Prof. S. Keller
• Hao Feng Tan