Question: I've run into a problem where I have two very large layers that I'm tasked to overlay on a map, showing cellular coverage across a the whole South Africa. 

Layer 1 was given to me in various formats and it was really easy to work via the Javascript API after getting it into a fusion table.

Layer 2 is my struggle currently, as it was given to me as a hosted KML that is updated automatically every 24 hours. The only reason it's a problem is because of its size, being 90MB!

I've explored several options including:

* Downloading and simplifying with ogr2ogr, but it drops the size with about 20MB's, which is still nowhere near ideal.
* QGIS - trying to work with the layer hangs the system
* pyKML and fastKML - both seem to be capable of reading from it but I'm not sure if this is the best option. I like working in Python and not afraid of a learning curve in parsing the correct data, but I haven't been able to find a clear example of this use case.
* Google Earth - Seems to be too big even for GE as it also doesn't show up or hangs the program.

I'd like to know if there's a way to simplify such a big file. Right now I can open it in QGIS and can see Layer 2 there, but like I said, trying to work with it is memory intensive. 

Can it be that the KML contains data that wasn't optimized for such a task? I'd like to be able to look at the KML data in a text editor, but even that hangs my Atom editor and others I've tried. I want to *see* what I'm working with in order to try and remove unnecessary stuff in there.

The url is here: <http://siteadmin.wbs.co.za/kml/coverage.kml>

I just need a few suggestions on how to handle this.

Answer: 
If you recognize the map area on google, then it is easy to pick-up the lat longs(though they may be cross-checked from known sources) from it and you can proceed with the georeferencing.

Best of luck,
sekhar
Details given by [Clifford C Mugnier](https://www.asprs.org/wp-content/uploads/2018/03/06-17-GD-Philippines.pdf) and the map collar:

Projection: Polyconic projection
Center of projection: 122° East
Map units: us-yards
False Easting: 1000000 yards
Datum: Luzon (approximate)

I get good soultions with this custom CRS:

```
+proj=poly +lat_0=0 +lon_0=122 +x_0=914401.8288036579 +y_0=0 +ellps=clrk66 +towgs84=-133,-77,-51,0,0,0,0 +units=us-yd +no_defs

```

The result is off about 100 meters, due to the "approximate" Luzon datum we do not know. and the scale factor of the projection is unknown too.