PostGIS Case Study: VozejkMap Open Data (Part I)
VozejkMap.cz is a Czech open data iniatitive that collects data about wheelchair accessible places, e.g. pubs, toilets, cafes etc. As part of being open, they offer a JSON data download. JSON is a great text format, not so great spatial format (leaving GeoJSON aside) though. Anyway, nothing that PostGIS wouldn’t be able to take care of.
Let’s get some data
Using curl or wget, let’s download the JSON file:
wget -O /tmp/locations.json http://www.vozejkmap.cz/opendata/locations.json
We need to split them into rows to load each point into one row:
sed -i 's/\},{/\n},{/g' /tmp/locations.json
If you peep into the file, you’ll see lots of unicode characters we don’t want to have in our pretty little table. Here’s how we get rid of them:
echo -en "$(cat /tmp/locations.json)"
Let’s load the data
Let’s just be nice and leave the public schema clean.
CREATE SCHEMA vozejkmap;
SET search_path=vozejkmap, public;
Load the data:
CREATE TABLE vozejkmap_raw(id SERIAL PRIMARY KEY, raw text);
COPY vozejkmap_raw(raw) FROM '/tmp/locations.json' DELIMITERS '#' ESCAPE '\' CSV;
A few notes:
- I’m using
/tmpfolder to avoid any permission-denied issues when opening the file frompsql. - By setting
DELIMITERSto#we tell PostgreSQL to load whole data into one column, because it is safe to assume there is no such character in our data. ESCAPEneeds to be set because there is one trailing quote in the dataset.
Let’s get dirty with spatial data
Great, now what? We loaded all the data into one column. That is not very useful, is it? How about splitting them into separate columns with this query? Shall we call it a split_part hell?
CREATE TABLE vozejkmap AS
SELECT
id,
trim(
split_part(
split_part(
raw, 'title:', 2
),
',location_type:', 1
)
) AS title,
trim(
split_part(
split_part(
raw, 'location_type:', 2
),
',description:', 1
)
)::integer AS location_type,
trim(
split_part(
split_part(
raw, 'description:', 2
),
',lat:', 1
)
) AS description,
cast( trim(
split_part(
split_part(
raw, 'lat:', 2
),
',lng:', 1
)
) AS double precision) AS lat,
cast( trim(
split_part(
split_part(
raw, 'lng:', 2
),
',attr1:', 1
)
) AS double precision) AS lng,
trim(
split_part(
split_part(
raw, 'attr1:', 2
),
',attr2:', 1
)
)::integer AS attr1,
trim(
split_part(
split_part(
raw, 'attr2:', 2
),
',attr3:', 1
)
) AS attr2,
trim(
split_part(
split_part(
raw, 'attr3:', 2
),
',author_name:', 1
)
) AS attr3,
trim(
split_part(
split_part(
raw, 'author_name:', 2
),
',}:', 1
)
) AS author_name
FROM vozejkmap_raw;
It just splits the JSON data and creates table out of it according to the VozejkMap.cz data specification. Before going on we should create a table with location types to join their numeric codes to real names:
CREATE TABLE location_type (
id integer PRIMARY KEY,
description varchar(255)
);
INSERT INTO location_type VALUES(1, 'Kultura');
INSERT INTO location_type VALUES(2, 'Sport');
INSERT INTO location_type VALUES(3, 'Instituce');
INSERT INTO location_type VALUES(4, 'Jídlo a pití');
INSERT INTO location_type VALUES(5, 'Ubytování');
INSERT INTO location_type VALUES(6, 'Lékaři, lékárny');
INSERT INTO location_type VALUES(7, 'Jiné');
INSERT INTO location_type VALUES(8, 'Doprava');
INSERT INTO location_type VALUES(9, 'Veřejné WC');
INSERT INTO location_type VALUES(10, 'Benzínka');
INSERT INTO location_type VALUES(11, 'Obchod');
INSERT INTO location_type VALUES(12, 'Banka, bankomat');
INSERT INTO location_type VALUES(13, 'Parkoviště');
INSERT INTO location_type VALUES(14, 'Prodejní a servisní místa Škoda Auto');
Let’s build some geometry column, constraints and indexes. And don’t forget to get rid of all the mess (the vozejkmap_raw table).
DROP TABLE vozejkmap_raw;
ALTER TABLE vozejkmap ADD PRIMARY KEY(id);
-- 4326 geometry is not very useful for measurements, I might get to that next time
ALTER TABLE vozejkmap ADD COLUMN geom geometry(point, 4326);
ALTER TABLE vozejkmap ADD CONSTRAINT loctype_fk FOREIGN KEY(location_type); REFERENCES location_type(id);
UPDATE vozejkmap SET geom = ST_SetSRID(ST_MakePoint(lng, lat), 4326);
And here we are, ready to use our spatial data!
Feel free to grab the code at GitHub.