Due to GDPR, you will have to log in with an OSM id to download the full history extracts. User ID’s are personal data.
Process:
The workflow is pretty simple. Osmium-tools provides pretty easy API access to the history files, where you can provide a data, and it will extract what OSM was like at that date. We simply need to loop through the desired dates we want to extract, and pipe the results into a workflow that loads the data into PostgreSQL. The final step is simply rendering in QGIS using the time manager plugin.
From these we can produce a GIF of hourly precipitation:
Hourly precipitation.
And total precipitation:
Particularly the hurricane path was possible to create in QGIS using the Atlas Generator, and the excellent new:ish geometry generator. This can be found as an option for any layers symbology, as one of the renderers.
For my map I had a non spatial table that drove my atlas. This was a log table of all of the hours of precipitation I had loaded into my database. So I looped through each entry and showed the corresponding points of hourly precipitation for the corresponding hour. I also had hurricane path data as points for every 6 hours. So I could use the geometry generator to interpolate points in between known points.
While the query ended up being pretty long it is pretty straightforward.
It only needs to be run when the hour being generated does not end with a 00, 06, 12, or 18, because those are the positions I already know.
For the rest I need to generate two points. One for the previously known point, and one for the next known point.
Then I would create a line between those two points, measure the line, and place a point on the line x times one sixth of the way for the start of the line depending on the hour from the last known point.
Overall I am very impressed and happy with the result. With a bit of data defined rotation the storm progress looks great.
A cool python script has been created that allows you to easily convert your google location (Takeout) data into a shapefile.
You can get your data from: Google Takeout
And you only need the “Location History – JSON format”
The conversion python script can be downloaded from: GitHub
The python script requires GDAL and its python bindings, but can be easily run if you installed QGIS using the OSGeo4W installer. From the advanced installer, under the Lib section.
OS MasterMap has an annotation layer, which is simple to symbolise in a GIS program. But becomes more difficult in CAD software.
With ogr2org, when writing a DXF file, if you have an input point geometry, which has an OGR_STYLE attribute, it will be written as a text geometry when opened in CAD.
So for our MasterMap data we have one layer we want to convert to text:
CartographicText
But lets try that as text. We will keep this simple and only take into account orientation and to a small extent height. Lets look at orientation:
Orientation – The orientation of text or symbol features for cartographic placement. This is measured in tenths of a degree anticlockwise from due east (0–3599).
So conversion to degree will be simple. Orientation/10
We can also take into consideration height as a multiplier.
And “textString” stores the text itself.
The command:
ogr2ogr.exe -f DXF CartographicText2.dxf os-mastermap-topography-layer-sample-data.gml CartographicText -sql "SELECT 'LABEL(f:""Arial"",s:""'||(height*800)||'"",t:""'||textString||'"",a:""'||(orientation/10)||'"",p:5)' AS OGR_STYLE, * FROM CartographicText" -dialect SQLITE
Full extent:
Zoomed in:
Explained:
Since this is run in windows, through the regular console, the escape character for quotes is two quotes “”‘. So a combination on ‘ ” and “”‘ we can accommodate all the required quotes.
Had to open data source read-only.
INFO: Open of 'os-mastermap-topography-layer-sample-data.gml'
using driver 'GML' successful.
1: TopographicArea (Polygon)
2: CartographicText (Point)
3: CartographicSymbol (Point)
4: BoundaryLine (Line String)
5: TopographicPoint (Point)
6: TopographicLine (Line String)
So we have 6 layers in total.
For MasterMap in CAD we will be mainly interested in CartographicText, TopographicPoint, and TopographicLine.
For this feature the “descriptiveGroup”” seems the most useful, and from reading the os-mastermap-topography-layer-user-guide.pdf the best would be either a combination of descriptiveGroup and descriptiveTerm or using the featureCode. Since this is a simple conversion we will just use a combo of descriptiveGroup and descriptiveTerm to create our DXF layers.
I will be using || for concatenation, which works with the SQlite SQL dialect.
layer names ignored in combination with -sql.
ERROR 1: No known way to write feature with geometry 'None'.
ERROR 1: Unable to write feature 0 from layer SELECT.
ERROR 1: Terminating translation prematurely after failed
translation from sql statement.
Not quite. Seems to be missing geometry, perhaps a SQL select issue.
This can be tested with:
ogrinfo os-mastermap-topography-layer-sample-data.gml TopographicLine -sql "select descriptiveGroup || ' - ' || descriptiveTerm as Layer from TopographicLine" -dialect SQLITE
Result:
OGRFeature(SELECT):14634
Layer (String) = Building - Outline
OGRFeature(SELECT):14635
Layer (String) = Building - Outline
So we do not have any geometry. Lets bring that in.
We can see that all of the attributes that are not 0 have both a descriptiveGroup and a descriptiveTerm, which was not what we can see in the ogrinfo summary. So our SQL statement is cutting some out.
Try again:
ogr2ogr -f DXF TopographicLine2.dxf os-mastermap-topography-layer-sample-data.gml TopographicLine -sql "select descriptiveGroup ||' - '|| coalesce(descriptiveTerm,'') as Layer, * from TopographicLine" -dialect SQLITE
Looking better:
But it won’t open in AutoCAD DWG TrueView. Lets try running it through a ShapeFile format first before the DXF conversion.
No indication of why a direct GML to DXF conversion would hang TrueView, and your mileage with other CAD software may vary. But ShapeFile is a very simplified geometry format, so perhaps running through that helps with some more complex geometry in the GML. Hard to say with no errors from TrueView, just a stuck program.
The power of GDAL, and specifically ogr2ogr is pretty impressive. This conversion is from shp to DXF, which is a somewhat universal CAD format so further conversion should be possible.
This post will cover contour export while maintaining 3D elevation, in addition to contour values as layers in CAD. The data used is OS terrain 50.
The alternative is to just store the z-value as layers.
ogr2ogr -f DXF contour_layer.dxf SX99SW_line.shp -sql "SELECT PROP_VALUE AS Layer FROM SX99SW_line"
Layers work great:
With the ogr2ogr DXF driver, if you have an input column called “Layer” then it will be used to group features as a layer in DXF. We use a SQL query to achive this. Prop_Value is the height field in my input data.
And putting them all together:
ogr2ogr -f DXF contour_zfield_layer.dxf SX99SW_line.shp -zfield PROP_VALUE -sql "SELECT PROP_VALUE AS Layer FROM SX99SW_line"
Result not as expected, flat output:
Adding our SQL select statement removes our zfield attribute as such ogr2ogr cannot access it. Lets resolve this:
ogr2ogr -f DXF contour_zfield_2_layer.dxf SX99SW_line.shp -zfield PROP_VALUE -sql "SELECT PROP_VALUE AS Layer, * FROM SX99SW_line"
Open the start menu, and search for: environment variables
Choose the Edit the system environment variables
Choose Environment Variables.
Edit the Path Variable by adding an entry for you path to ogr2ogr.
I would recommend installing ogr2ogr using the OSGeo4W network installer and the default path. If you have done this the path is: C:\OSGeo4W\bin
You also need to add 2 new entries into the base environmental variables, one for:
GDAL_DATA
With path:
C:\OSGeo4W\share\gdal
One for:
PROJ_LIB
With path:
C:\OSGeo4W64\share\proj
Adjust the paths accordingly if you have not used the default install locations.
Windows 10
I recently upgraded to Windows 10, and have been setting up my GIS workspace settings.
Setting up your Windows path environmental variables can easily be done through the command line.
These commands can be run directly in the command line and don’t require a computer restart to take effect. To run the command prompt, simply open up the windows search (Windows Key + S) and search for “cmd”:
Then run the command to add the path.
Update: PROJ_LIB also needs to be set, to prevent “PROJ: proj_create_from_wkt: Cannot find proj.db” errors.
Determine where your QGIS is installed. I recommend using the OSGeo4W network installer and using the default location. Which would be: C:\OSGeo4W\bin
Since QGIS 3.26 this can now be achieved with the georeferencer, same tool that is used of raster georeferencing:
QGIS now supports georeferencing vector layers in the georeferencer tool. This allows vector layers without spatial referencing to be interactively georeferenced, or layers with referencing to be re-referenced, in a similar manner to raster data. Georeferencing occurs in a task, so QGIS should remain responsive, even with large datasets.
Georeferencing vector files using ogr2ogr, search for “The georeferencing:” to skip the other steps.
Georeferencing vector data has long been very difficult using the open-source GIS stack. That has all changed with the release of GDAL 1.10.0. Now georeferencing can be done using the vector translator ogr2ogr.
About half a decade ago I wrote my undergraduate dissertation on: “The influence of the Blaeu Atlas of Scotland on subsequent maps of Scotland”. While I was very proud of the work at the time, and the grade was good, the markers comments could be paraphrased as: A very good literature review on the Blaeu Atlas, however somewhat weak on analysis”. After a Master’s degree I could not agree more. At the time I lacked the knowledge and experience of working with projections. The release of GDAL 1.10.0 gives the perfect opportunity to return and correct those mistakes.
One of my favourite aspects of the Blaeu analysis was a comparison of the Blaeu 1654 Atlas of Scotland coastline to the coastline of subsequent maps of Scotland. I originally did this in GIMP and manually resized the outlines to close approximations. The end result looked good, but it lack real scientific rigour.
Originally the maps were provided as .jpeg files with a simple viewer used for zooming. This meant that editing the URL would allow one to retrieve a very large version of the of the map directly. Now the maps are served using a a very nice javascript map viewer and digital copies can be purchased.
First we need to create a vector outline from the raster image. This is a simple case of adding the .jpeg image in as a raster file. When prompted for a CRS I chose EPSG:4030 “Unknown datum based upon the WGS 84 ellipsoid” this is just used for meta-data at this point. What you choose does not matter. We just want a vector file in cartesian co-ordinates. We create a new shapefile layer and trace outline. My co-ordinate capture will be very rough. If you are working with more modern maps, you should be as careful as required:
Whenever I needed a break I would end the line. Then I could start a new line snapped to the end of the last one. In the end you can select all of the line segments and “merge selected features” from the advanced digitising toolbar. This would likely have been enough, but I was concerned there would be two overlapping nodes and that I would have a multi-part line. So I exploded the lines into points using the Vector>Geometry Tools>Extract nodes tool. Then Using the handy Points2One plugin (thanks Pavol Kapusta) I could stitch the nodes back together into a single line. If you’re going to do something might as well do it correctly. In the end we have 5116 nodes:
With the Blaeu outline captured we need a basemap to georeference it to. In the end we want to re-projectiong it into the WGS 84 / World Mercator projection (EPSG:3395). Mercator will provide a good base of what the original mapper would have been trying to capture. In the UK we can turn to the Ordnance Survey for a basemap, however that basemap needs to be reprojected to EPSG:3395. This is simply done in within QGIS by selecting the “Enable on-the-fly CRS transformation” to EPSG:3395 from Project>project Properties>CRS tab.
From the Ordnance Survey (OS) we have a few open datasets to choose from:
OS BoundaryLine (High Water Polyline)
OS BoundaryLine (european electoral regions)
We can see how they line up with the raster products:
OS MiniScale
OS 250K
We can see that the High Water Polyline follows MiniScale much better than the electoral regions:
It doesn’t line up perfectly with the 250k raster (250k is probably too accurate for this study):
But on the whole it will serve for this purpose, in addition Scotland can be easily extracted:
Now we have the High Water Polyline, which looks good, but we only want mainland Scotland. Easy right?
Well… slighlty more complex. Unfortunately even the Scottish high water lines consisted of 16030 line segments. Select by location in QGIS using the European Electoral Regions (polygon) took too long (hours rather than minutes). So I had to resort to PostGIS to do the selection.
The query in PostGIS/PostgreSQL to select all of the lines within a polygon (we use the OS boundary line electoral regions):
CREATE TABLE scotland_selection AS
SELECT os_high_water_mark.* FROM os_high_water_mark,european_scotland
WHERE ST_Intersects(european_scotland.the_geom,os_high_water_mark.the_geom);
Result:
Query returned successfully: 4994 rows affected, 764779 ms execution time.
The 12 minute run time says a lot about my poor computer.
Then the lines are joined together, the geometries simplified, and multipart converted to singlepart. This allows us to get rid of the final islands. With the result back in QGIS:
Closeup:
Final result projected into WGS 84 / World Mercator projection (EPSG:3395), which really shows the distortion caused by the British National Grid (EPSG:27700):
We now have a cartesian vector file ready for georeferencing, an OS raster background map to use for georeferencing (OS MiniScale) and an OS vector outline BoundaryLine (High Water Polyline) to use for the final comparison.
The georeferencing:
We open up two QGIS projects. In one we have our cartesian vector file. In the other we have our basemap:
Using the co-ordinate capture tool, we capture the same point from both projects. Basically we are just capturing co-ordinate pairs from the two maps. The “Copy to clipboard” feature comes in very handy here.
These captured co-ordinates should be pasted into a text file. Something like this:
Of note here is that the co-ordinate capture tool captured the co-ordinate twice for me. How it works is that it captures one in the original CRS and the selected one. It is also important to keep your co-ordinates in order. I captured the cartesian co-ordinates on the first row and the WGS84 ones on the second row. Also I have attached memorable names to the points, in case there is an issue, the culprit point can be easily be identified.
For my project 23 points were captured:
After the points are captured we switch to ogr2ogr to perform the actual reprojection and to convert the OS outline into World Mercator.
Scotland Mainland Outline from Boundary Line High Water Mark to World Mercator (while I’m converting I will convert it to a GeoJSON for viewing in leaflet (this will be covered by a later post) and strip out any attributes to decrease the file size):
![Screenshot[10]](https://gisforthought.com/media/2015-11-02_21555945593_de453d5cf4_o.png)
![Screenshot[11]](https://gisforthought.com/media/2015-11-02_21988887020_9499dc8d12_o.png)
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![Screenshot[1]](https://gisforthought.com/media/2015-10-19_21988888570_a64bb416f1_o.png)
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![Screenshot[3]](https://gisforthought.com/media/2015-08-10_20428438032_845361b63c_z.jpg)
![Screenshot[6]](https://gisforthought.com/media/2015-08-10_20443327641_f351605c55_o.png)
![Screenshot[7]](https://gisforthought.com/media/2015-08-10_20250461329_94fe43f009_o.png)
