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About
Where it all begins.
Aug 6, 2024
Visualizing cloud native data directly from source FGB file(s) with only your browser is the simplest, most cost-efficient and easiest to maintain, by far.
The basic architecture requires a @flatgeobuf source and the most basic HTML and vanilla JavaScript. CSS optional. Other than the .fgb file(s) sitting on an out-of-the-box web server or cloud source such as AWS S3, there is no back-end.
Visualizing static raster and vector data is by far the most common task GIS users do with their browser, whether investigating new data or visualizing a finished product.
Let's visualize some very large data sets. Yes, I'm talking about 146M addresses and 58M parcels from OpenAddresses.io . We'll also be using 145M building footprints from Source.Coop to drive the visualization point home. Address and parcel data are not available everywhere, but the coverage is still exceptional.
We'll be using 3 remote @flatgeobuf files (addresses, parcels, buildings) on a basic web server and your browser with vanilla JavaScript and basic HTML/CSS. Again, no back-end.
To the point, you can visualize these CONUS data sets, initially focused on San Francisco's Embarcadero district:
https://www.femafhz.com/map/37.806364/-122.416706/17/footprints,addresses,parcels
Source.coop had the vision to make their global building footprint data sets available by country, in @flatgeobuf format. It's just a matter of downloading it and using it.
OpenAddresses.io has a number of data sets, global, Canada and US regional. We'll be getting the US regions, which are zipped .geojson files with a directory structure of ./us/[state]
In each state directory are county .geojson/.meta files, address, parcel and buildings. There is also a 'statewide' geojson file in some states, not all. So we need to weed those out and use the county data sets only.
Initially, instead of working with the .geojson data directly, I'm going to create virtual vector data sets in .vrt format for each state. This is a popular @GdalOrg format. It's also faster and more flexible for later data use.
Here's an ugly one-liner to create all the commands needed to create a .vrt for each state, sans Alaska and Hawaii. Each .vrt will be created in the directory 'vrt', so create it first.
find . -type d | sed "s/[^a-z]//g" | grep -v statewide | sort | awk '{ if(length($1)) printf("ogrmerge.py -f VRT -o /full/path/vrt/%s_addresses.vrt -single -nln addresses /full/path/us/%s/*addresses*.geojson\n", $1, $1); }'| | grep -v "hi_" | grep -v "ak_" > mkVrt.sh
Now just run: bash mkVrt.sh
Do the same thing with parcels. Change every instance of 'addresses' to 'parcels'.
Now that we have all the data virtually by state, let's combine them into a single CONUS vrt.
ogrmerge.py -f VRT -o addresses.vrt -single -nln addresses /full/path/vrt/*.vrt
Do the same for 'parcels'. You should now have parcels.vrt and addresses.vrt
Now, let's create the .fgb file, your system will need to open ~2,200 files:
ogr2ogr -nln addresses -nlt POINT -makevalid -where "street not like '' and number not like ''" -spat -126 23 -65 50 addresses.fgb addresses.vrt
Do the same for 'parcels', except change the -nlt to MULTIPOLYGON as parcels are not point geometries.
OpenAddresses.io is a fantastic resource. Hopefully, in the future, we'll get a more organized set of data. A big thanks to them for making this data available!
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