Code for deduplicating OpenAddresses data, currently U.S. English only. Outputs CSV files with rows for matched groups of addresses and point features for unmatched single addresses.
In this example image of Butte, MT dark-colored linestrings show addresses that have been matched between county and statewide sources, while magenta dots show addresses that have not been matched all. This shows that most normal addresses in Butte are correctly deduped between county and statewide sources:
Sample usage to install required packages, download 100,000 rows of sample data from OpenAddresses, and generate a set of CSV files using GNU Parallel:
$ pip3 install -r requirements.txt
$ ./address-areas.py /dev/stdout | head -n 100000 | ./address-map.py > 100k-filenames.txt
$ parallel -a 100k-filenames.txt ./expand-reduce.py '{}' '{.}.csv'
Addresses are deduped within the areas found in geodata/areas.shp matching
U.S. Census defined CBSA's and excluded state areas.
$ apt-get install parallel
$ ./split-areas.py > split-areas.txt
$ parallel -a split-areas.txt ./address-areas.py --areas '{}' '{.}.txt'
$ parallel -a split-areas.txt ./address-map.py '{.}.txt' | sort | uniq > area-filenames.txt
$ parallel -a area-filenames.txt ./expand-reduce.py '{}' '{.}.csv'
These samples were all run on a Virtualbox virtual machine: Ubuntu 14.04, 1x CPU, 2GB memory, Python 3.4, and PostGIS.
All of Santa Clara Valley:
- 728,279 address rows.
- 52,629 comparison tiles sorted.
- 477,829 output features: 228,134 (65.5%) merged.
- 376,003 address rows in 0:35 minutes (57% cpu).
- 252,644 comparison tiles sorted in 1:15 minutes (19% cpu).
- 228,426 output features: 135,896 (63.9%) merged in 1:32 minutes (56% cpu).
All of Montana:
- 1,011,515 address rows in 1:37 min (59% cpu).
- 702,745 comparison tiles sorted in 3:29 min (20% cpu).
- 603,294 merged features: 335,835 (66.8%) merged in 36:23 min (21% cpu).
Merging took a long time when expand-reduce.py thrashed on low physical RAM.
Running this in parallel would solve the problem.