A few of my students wanted to use PostGIS to help them easily query a Postgres database for all the points within a certain distance of a given location. Getting them up and running proved a bit of a struggle, so I decided to put together a little tutorial for others who might be interested.
The first half of this tutorial covers working with PostGIS in pure SQL, and the second half covers using PostGIS in Python with SQLAlchemy, GeoAlchemy2, and Flask-SQLAlchemy. If none of the Python bits interest you, then you can still learn something from the material before the "Use PostGIS with SQLAlchemy" section. :)
You should have Postgres, Python, pip, and virtualenv installed on your to complete this tutorial. For the Python bit, I used Python 2.7.
Since PostGIS is the focus of this tutorial, I also assume you have some experience using SQLAlchemy for the ORM portion.
I assume you've already installed Postgres itself on your machine and that
you've figured out how to set up the psql command to run Postgres from
the command line.
For all operating systems, first open the Postgres command line interface:
$ psql <your database name>
From inside the command line interface, use the CREATE EXTENSION syntax
desrcibed on the PostGIS website to try to add the PostGIS extension to your
database.
CREATE EXTENSION postgis;If you see an error along the lines of:
ERROR: could not open extension control file "path/to/file": No such file or directory
then you don't have PostGIS installed yet. Fortunately, the setup instructions on the PostGIS website are pretty straightforward. Since I've installed PostGIS on OSX and Ubuntu, however, I do some advice there.
I highly recommend using the Postgres.app version of Postgres, as it comes with PostGIS already.
If your version of Postgres doesn't include PostGIS, then you're likely just an
apt-get away from having it. Run:
sudo apt-get install postgis
If you run into an error that mentions some dependencies not getting installed, update apt-get:
sudo apt-get update
And then try installing again.
This link (found originally on the PostGIS website) was pretty helpful.
However you arrive at your PostGIS installation, you should now be able to run
the CREATE EXTENSION command. Go into psql now and try it.
Once you have PostGIS set up, I suggest tinkering around in pure SQL for a bit so you're familiar with how querying should work.
Go into psql now and make yourself a table to play around with. I kept mine simple:
CREATE TABLE cities (
point_id SERIAL PRIMARY KEY,
location VARCHAR(30),
latitude FLOAT,
longitude FLOAT,
geo geometry(POINT)
);This SQL creates a table called cities with an automatically incrementing integer primary key. It has a column for the location (limited to 30 characters long), the latitude, and the longitude. The geo column is where our PostGIS point data will go. I chose to use the geometry type here because I read that it would be the simplest to work with.
Next, add some data to your table. You could do this by hand with INSERT statements, or you could load data from a CSV file that includes latitudes and longitudes.
To save you some manual typing, I'll show you how to get some seed data from a CSV file to start.
First, find yourself a CSV file. You could do a quick search on a site like ProgrammableWeb for some data that intrigues you, or you can copy this CSV-formatted text:
location, latitude, longitude San Francisco, 37.773972, -122.43129 Seattle, 47.608013, -122.335167 Sacramento, 38.575764, -121.478851 Oakland, 37.804363, -122.271111 Los Angeles, 34.052235, -118.243683 Alameda, 37.7652, -122.2416
Notice that while the cities table has a geo column, this data lacks information for that column. That's perfectly fine; in fact, it's intentional.
If you copy this example data, just paste it into a file with the .csv extension. I called mine postgis.csv.
Once you have a CSV file, go back to your psql shell and enter the following command to load the data into your cities table:
\copy cities(location, latitude, longitude) FROM 'postgis.csv' DELIMITERS ',' CSV HEADER;This uses Postgres' copy command to fill the location, latitude, and longitude columns in the cities table with the corresponding data from the CSV file. I was able to just give a filename because the file was in the directory I was in when I opened the psql shell; if your CSV isn't in your current working directory, then you'll need to give a full file path. The DELIMITERS value tells Postgres what the data is separated by, CSV indicates the file type, and HEADER indicates that the file has column headers.
After seeding with this information, try selecting everything from the cities table:
SELECT * FROM cities;You should see output like this:
point_id | location | latitude | longitude | geo
----------+---------------+-----------+-------------+-----
1 | San Francisco | 37.773972 | -122.43129 |
2 | Seattle | 47.608013 | -122.335167 |
3 | Sacramento | 38.575764 | -121.478851 |
4 | Oakland | 37.804363 | -122.271111 |
5 | Los Angeles | 34.052235 | -118.243683 |
6 | Alameda | 37.7652 | -122.2416 |
(6 rows)
Now that you have some latitudes and longitudes to work with, let's get some data into that geo column. Run the following UPDATE command:
UPDATE cities
SET geo = ST_Point(longitude, latitude);The ST_Point function takes a longitude and a longitude and creates a blob that represents that point in a given coordinate system. By default, ST_Point uses the WGS84 format, which is the same standard used for GPS. You can read more about ST_Point in the PostGIS docs
(If you need to use a different coordinate system, you'll need to change the spatial reference system identifier (srid) on your column. The ST_SetSRID function can help with that.)
If you select everything from cities, you should now see output like this:
point_id | location | latitude | longitude | geo
----------+---------------+-----------+-------------+--------------------------------------------
1 | San Francisco | 37.773972 | -122.43129 | 0101000000E1455F419A9B5EC08602B68311E34240
2 | Seattle | 47.608013 | -122.335167 | 0101000000B3EC496073955EC07C45B75ED3CD4740
3 | Sacramento | 38.575764 | -121.478851 | 01010000000B2AAA7EA55E5EC0691B7FA2B2494340
4 | Oakland | 37.804363 | -122.271111 | 01010000007FA5F3E159915EC0658EE55DF5E64240
5 | Los Angeles | 34.052235 | -118.243683 | 0101000000D6E59480988F5DC0715AF0A2AF064140
6 | Alameda | 37.7652 | -122.2416 | 0101000000ACADD85F768F5EC01973D712F2E14240
(6 rows)
Cool! We've got some data. Don't worry if you can't make any sense of the contents of the geo column. PostGIS will take care of it.
Eventually, you might also want to add a new city complete with its geometry data without using an UPDATE statement. Here's how:
INSERT INTO cities (location, latitude, longitude, geo)
VALUES ('San Bruno', 37.6305, -122.4111, 'POINT(-122.4111 37.6305)');The string passed for the geo column is written in Well-Known Text, a language used to communicate vector geometries.
You could also make your point like this:
INSERT INTO cities (location, latitude, longitude, geo)
VALUES ('San Rafael', 37.9735, -122.5311, ST_Point(-122.5311, 37.9735));Here, the ST_Point function makes a point out of the longitude and latitude.
Now that you have some geospatial data stored with PostGIS, you can ask for all points within a given distance of a particular point. Let's ask for all cities within 50 miles of San Francisco.
SELECT * FROM cities
WHERE ST_Distance_Sphere(geo,
(SELECT geo FROM cities WHERE location = 'San Francisco')
) < 83000;The ST_Distance_Sphere gives a linear distance between two given points, as described here. The distance it returns is in meters, so if you're working in miles, you'll need to convert. I used an SQL subquery to get San Francisco's geometry blob, but you could hard code, too.
Your results should look something like this:
point_id | location | latitude | longitude | geo
----------+---------------+-----------+-------------+--------------------------------------------
1 | San Francisco | 37.773972 | -122.43129 | 0101000000E1455F419A9B5EC08602B68311E34240
4 | Oakland | 37.804363 | -122.271111 | 01010000007FA5F3E159915EC0658EE55DF5E64240
6 | Alameda | 37.7652 | -122.2416 | 0101000000ACADD85F768F5EC01973D712F2E14240
8 | San Rafael | 37.9735 | -122.5311 | 0101000000F5B9DA8AFDA15EC0F853E3A59BFC4240
9 | San Bruno | 37.6305 | -122.4111 | 0101000000AED85F764F9A5EC062105839B4D04240
(5 rows)
Sacramento, Los Angeles, and Seattle have all been filtered out, as they should. Hooray!
From here, I'll leave it to you to poke around the PostGIS docs a bit, try out some other functions, and so on. When you're ready to try integrating PostGIS with SQLAlchemy, read on.
If you don't want to live in a pure SQL world anymore, you can also use PostGIS via an ORM. I'm most comfortable with SQLAlchemy after my work at Hackbright, so that's what I'm using.
First, create a virtual environment, activate it, and install the following requirements:
click==6.7 Flask==0.12.2 Flask-SQLAlchemy==2.3.2 GeoAlchemy2==0.4.0 itsdangerous==0.24 Jinja2==2.10 MarkupSafe==1.0 psycopg2==2.7.3.2 SQLAlchemy==1.1.15 Werkzeug==0.12.2
Flask-SQLAlchemy makes working with SQLAlchemy a bit nicer, and GeoAlchemy2 is the package that allows us to use PostGIS.
We'll need to import a few things and create a couple of global objects before we can begin. Open a new Python file and add this to the top:
from flask import Flask
from flask_sqlalchemy import SQLAlchemy
from sqlalchemy import func
from geoalchemy2 import Geometry
app = Flask(__name__)
db = SQLAlchemy()We need Flask to create an application context to bind our SQLAlchemy session to. The lowercase sqlalchemy (and lowercase is key here) import, func, will allow us to execute PostGIS functions and other SQL functions that aren't exposed otherwise through the SQLAlchemy model. The Geometry class imported from geoalchemy2 will let us make our geospatial column.
Now, let's make an SQLAlchemy model class to work with. Add this code to your Python file:
class City(db.Model):
"""A city, including its geospatial data."""
__tablename__ = "cities"
point_id = db.Column(db.Integer, primary_key=True, autoincrement=True)
location = db.Column(db.String(30))
longitude = db.Column(db.Float)
latitude = db.Column(db.Float)
geo = db.Column(Geometry(geometry_type="POINT"))
def __repr__(self):
return "<City {name} ({lat}, {lon})>".format(
name=self.location, lat=self.latitude, lon=self.longitude)
def get_cities_within_radius(self, radius):
"""Return all cities within a given radius (in meters) of this city."""
return City.query.filter(func.ST_Distance_Sphere(City.geo, self.geo) < radius).all()
@classmethod
def add_city(cls, location, longitude, latitude):
"""Put a new city in the database."""
geo = 'POINT({} {})'.format(longitude, latitude)
city = City(location=location,
longitude=longitude,
latitude=latitude,
geo=geo)
db.session.add(city)
db.session.commit()
@classmethod
def update_geometries(cls):
"""Using each city's longitude and latitude, add geometry data to db."""
cities = City.query.all()
for city in cities:
point = 'POINT({} {})'.format(city.longitude, city.latitude)
city.geo = point
db.session.commit()This model represents the same data as the cities table from earlier. It has the same columns and types, but we define the type of the geo column using GeoAlchemy2 syntax.
When I went through this process, I used the copy command described in the
"Copy from a CSV File" section to get my city and point data into the table.
I tried to also use the UPDATE statement to add the geometries since I had it
conveniently typed out, but unfortunately, when I queried for objects in the
Python terminal, I only got back None for the geo column. I added the
update_geometries() method to create points as strings and add the geometries
through SQLAlchemy and GeoAlchemy2. It seems when you do this from
within the ORM, the geospatial data gets turned into a WKElement object when
it's added to the record.
The get_cities_within_radius() method shows the syntax for querying for all points within a given radius (our stated goal at the beginning). Let's break it down.
- SQLAlchemy's func lets us access the ST_Distance_Sphere function we used when we were still working in pure SQL.
- ST_Distance_Sphere takes two points and returns how far apart those points are.
From here, everything is just SQLAlchemy. We compare the number returned by ST_Distance_Sphere against the passed radius, use that condition in a filter clause, query the whole table, and ask for all results found.
At the end of your Python file, add the following code to help you actually use your model:
def connect_to_db(app):
"""Connect the database to Flask app."""
app.config['SQLALCHEMY_DATABASE_URI'] = 'postgres:///yourdatabasename'
app.config['SQLALCHEMY_ECHO'] = False
app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False
db.app = app
db.init_app(app)
if __name__ == "__main__":
connect_to_db(app)
db.create_all()
print "Connected to database."The connect_to_db() function sets some config variables and connects
our app to the database. (Needed here because we're using Flask-SQLAlchemy.)
Be sure to replace "yourdatabasename" in the URI definition with the correct
name for your database. The ECHO and TRACK_MODIFICATIONS variables are set
to False to turn off some features for the moment.
Under the if __name__ == "__main__" line, we tell Python to connect to the
database, create all tables, and give a helpful message when the file is
run from the command line.
Run your model file interactively with python -i model.py now to make sure
your code runs without error.
At this point, you should have:
- Created a database
- Written a model.py file
- Loaded your model.py file in Python and connected to the database
Now, we can play with our city records in the terminal. Try these snippets in the interactive console:
>>> for city in City.query.all():
... print city
...
<City San Francisco (37.773972, -122.43129)>
<City Seattle (47.608013, -122.335167)>
<City Sacramento (38.575764, -121.478851)>
<City Oakland (37.804363, -122.271111)>
<City Los Angeles (34.052235, -118.243683)>
<City Alameda (37.7652, -122.2416)>
>>> sb = City(location='San Bruno',
... longitude=-122.4111,
... latitude=37.6305,
... geo='POINT(-122.4111 37.6305)')
>>> db.session.add(sb)
>>> db.session.commit()
>>> sb.geo
'POINT(-122.4111 37.6305)'
>>> sf = db.session.query(City).filter(City.location == 'San Francisco').one()
>>> sf
<City San Francisco (37.773972, -122.43129)>
>>> sr = City(location='San Rafael',
... longitude=-122.5311,
... latitude=37.9735,
... geo=func.ST_Point(-122.5311, 37.9735))
>>> sr
<City San Rafael (37.9735, -122.5311)>
>>> sr.geo
<sqlalchemy.sql.functions.Function at 0x107817150; ST_Point>
>>> db.session.add(sr)
>>> db.session.commit()
>>> sr.geo
<WKBElement at 0x107788a10; 0101000000f5b9da8afda15ec0f853e3a59bfc4240>
>>> fifty_miles_in_meters = 83000
>>> ten_miles_in_meters = 16093.4
>>> nearish_cities = sf.get_cities_within_radius(ten_miles_in_meters)
>>> farish_cities = sf.get_cities_within_radius(83000)
>>> for city in nearish_cities:
... print city
...
<City San Francisco (37.773972, -122.43129)>
<City Oakland (37.804363, -122.271111)>
<City San Bruno (37.6305, -122.4111)>
>>> for city in farish_cities:
... print city
...
<City San Francisco (37.773972, -122.43129)>
<City Oakland (37.804363, -122.271111)>
<City Alameda (37.7652, -122.2416)>
<City San Bruno (37.6305, -122.4111)>
<City San Rafael (37.9735, -122.5311)>
>>> City.add_city("Sausalito", -122.4853, 37.8591)
>>> City.add_city("Daly City", -122.4702, 37.6879)
>>> City.add_city("San Jose", -121.8863, 37.3382)
>>> City.add_city("Vallejo", -122.2566, 38.1041)
>>> City.add_city("Orlando", -81.3815, 28.5469)
>>> City.add_city("New York City", -73.9603, 40.7666)
>>> cities_within_ten_miles = City.query.filter(
... func.ST_Distance_Sphere(City.geo, sf.geo) < ten_miles_in_meters).all()
>>> for city in cities_within_ten_miles:
... print city
...
<City San Francisco (37.773972, -122.43129)>
<City Oakland (37.804363, -122.271111)>
<City San Bruno (37.6305, -122.4111)>
<City Sausalito (37.8591, -122.4853)>
<City Daly City (37.6879, -122.4702)>
>>> # Order the cities by distance from SF.
>>> cities_within_ten_miles = City.query.filter(
... func.ST_Distance_Sphere(City.geo, sf.geo) < ten_miles_in_meters).order_by(
... func.ST_Distance_Sphere(City.geo, sf.geo)).all()
>>> for city in cities_within_ten_miles:
... distance = db.session.query(func.ST_Distance_Sphere(city.geo, sf.geo)).one()[0]
... print "{} is {} meters from SF".format(city.location, distance)
...
San Francisco is 0.0 meters from SF
Daly City is 10164.110173 meters from SF
Sausalito is 10588.2148564 meters from SF
Oakland is 14475.5833668 meters from SF
San Bruno is 16051.9613992 meters from SFThe cities ultimately returned by get_cities_within_radius() seem correct enough to be getting on with, and when I ordered by the distance apart and printed the distances, they seem close. Google Maps says Daly City is a 7.6 mile drive from San Francisco, which converts to about 12231 meters. I'd believe that the drive would take an extra couple thousand meters (about 1.2 miles) compared to a pure distance measurement.
If you've gotten this far, then congrats: you have PostGIS working with Flask and SQLAlchemy!
I put together this tutorial after much debugging with fellow staff members at Hackbright on a few student projects this cohort. We would likely have spent much more time beating our heads against PostGIS without referencing a past student project: Joanne Yeung's Investable. Joanne's excellent documentation of the PostGIS setup process inspired me to take things a step further and actually write up a tutorial.
The rest of this section lists some docs, posts, and other resources I found helpful throughout the debugging process.
- Querying for points within a certain distance
- Inserting a point into PostGIS
- Usage of ST_SetSRID, etc.
- Using ST_DWithin (It wound up making more sense to use ST_Distance_Sphere instead, but this syntax example was helpful.)
- Blog post where I got the idea to use a CSV and copy
Hope you've found this tutorial helpful! @ me on Twitter or something if you did. :)