I have a PostGIS-enabled database with a table called locations that stores latitude-longitude points (SRID 4326) in a column called coordinates. However, all of my lookups on that table convert the points to a metric projection (SRID 26986) mainly to do distance comparisons.
Obviously I'd like to create a spatial index on the coordinates column. My question is, which is the best (most computationally efficient) SRID to use in the coordinates spatial index in this case?
I can either index using SRID 4326...
CREATE INDEX locations_coordinates_gist
ON locations
USING GIST (coordinates);
Or using SRID 26986...
CREATE INDEX locations_coordinates_gist
ON locations
USING GIST (ST_Transform(coordinates, 26986));
I discovered this helpful information reading the PostGIS documentation on the ST_Transform function...
If using more than one transformation, it is useful to have a
functional index on the commonly used transformations to take
advantage of index usage.
So it seems the answer is, use both! I have created two indices, one with each SRID.
Related
I have a table "ways" containing coordinates (lat/lon) values. Suppose I have a coordinate (x,y) and I want to check the closest match from the table ways. I have seen some similar questions like this: Is there a postgres CLOSEST operator?
However in my case I have 2 columns instead of 1. Is there a query to do something like this this ?
You could store the data as PostGIS 'point' type instead of coordinate values:
https://postgis.net/docs/ST_MakePoint.html
https://postgis.net/docs/ST_GeomFromText.html
This would empower you with all the PostGIS functionality such as:
https://postgis.net/docs/ST_DWithin.html
Then you could create a GiST index and use the PostGIS <-> operator to take advantage of index assisted nearest neighbor result sets. The 'nearest neighbor' functionality is pretty common. Here is a great explanation:
https://postgis.net/workshops/postgis-intro/knn.html
“KNN” stands for “K nearest neighbours”, where “K” is the number of neighbours you are looking for.
KNN is a pure index based nearest neighbour search. By walking up and down the index, the search can find the nearest candidate geometries without using any magical search radius numbers, so the technique is suitable and high performance even for very large tables with highly variable data densities.
I have a PostGreSQL table that has a geometry type column, in which different simple polygons (possibly intersecting) are stored. The polygons are are all areas within a city. I receive an input of a point (latitude-longitude pair) and need to find the list of polygons that contain the given point. What I have currently:
Unclustered GiST index defined on the polygon column.
Use ST_Contains(#param_Point, table.Polygon) on the whole table.
It is quite slow, so I am looking for a more performant in-memory alternative. I have the following ideas:
Maintain dictionary of polygons in Redis, keyed by their geohash. Polygons with same geohash would be saved as a list. When I receive the point, calculate its geohash and trim to a desired level. Then search in the Redis map and keep trimming the point's geohash until I find the first result (or enough results).
Have a trie of geohashes loaded from the database. Update the trie periodically or by receiving update events. Calculate the point's geohash, search in the trie until I find enough results. I prefer this because the map may have long lists for a geohash, given the nature of the polygons.
Any other approaches?
I have read about libraries like GeoTrie and Polygon Geohasher but can't seem to integrate them with the database and the above ideas.
Any cues or starting points, please?
Have you tried using ST_Within? Not sure if it meets your criteria but I believe it is meant to be faster than st_contains
I'm using Postgres (with PostGIS) and I have geometry columns which have the SRID of 4326. In my stack, Redis is acting as a cache and holds the location data using it's geo functionality, specifically, let's say I have a location in Postgres like so:
INSERT INTO locations(geom) VALUES(ST_GeomFromText('POINT(40.7547 -73.9614)', 4326)) RETURNING 'id';
Then the object gets added to Redis:
redis> GEOADD someId -73.9614 40.7547
The question is, because Redis uses a totally spherical model of the globe, is it beneficial to use a similarly spherical model SRID for postgres? I found EPSG:4047 which is a sphere.
Are there time, accuracy, or other benefits or pitfalls I'm not seeing?
Keep the data in EPSG:4326, if all computations are to be done in the Redis environment it won't matter; you cannot pass in spatial reference information, and Redis seems to assume the WGS84 based (Spherical) Web Mercator projection ('seems' because they state to use a wrongly assigned and deprecated EPSG code for it...), at least for their indexing implementation.
The distance calculation via Haversines is unaffected by changing between those reference systems (and the CRS that Redis uses); the formulae work independent from any spatial reference other than the spherical radius.
I am using PostGis for Location based calculations in my Application. In a Table i have a column called 'location' in geography type(Point(lon lat))...Like this number of rows present in the Table.
I want to pass a point(Point(lon lat)) and check distance between this point(i passed) and location column in all rows....and if distance is less than 5 m....it will return the name of the point.How to query this.
Assuming that your srid of your data is 4326 the query you are looking for is:
SELECT the_geom FROM mytable WHERE ST_DWithin(the_geom,ST_GeomFromEWKT("srid=4326;POINT(lon lat)"), 0.0008);
Note that the units(0.0008) in ST_DWithin are in the same units of your projection, in the 4326 case they are degrees. If your projection data is in meters, you will be able to use meters.
For a production application you should use geometry types, is faster. From a stackoverflow previous question:
Short Answer: geography is a new data type that supports long range
distances measurements. If you use geography -- you don't need to
learn much about planar coordinate systems. Geography is generally
best if all you care about is measuring distances and lengths and you
have data from all over the world. Geometry datatype is an older data
type that has many functions supporting it and enjoys great support
from third party tools. Its best if you are pretty comfortable with
spatial reference systems or you are dealing with localized data where
all your data fits in a single spatial reference system (SRID), or you
need to do a lot of spatial processing. Refer to Section 8.8, “PostGIS
Function Support Matrix” to see what is currently supported and what
is not.
Great.Thank You. It works fine in database. I have following code from PHP..it returs like, Query Failed:
$locationresult=pg_query($con,"SELECT id,name FROM gps.locationnames WHERE ST_DWithin(location,ST_GeographyFromText('POINT(lon lat)'),500)") or die ('Query Failed:'.pg_last_error($con));
What is the problem here..
I'm using PostgreSQL with PostGIS. All my data has already decimal lat/long attached to it (i.e. -87.34554 33.12321) but to use PostGIS I need to convert it to a certain type of SRID.
The majority of my queries are looking for data inside a certain radius.
What SRID should I use? I created already a geometry column with SRID 4269.
In this example:
link text the author is converting SRID 4269 to SRID 32661. I'm very confused about how and when to use these SRIDs. Any lite on the subject would be truly appreciated.
As long as you never intend to reproject/transform the data to another coordinate system, it doesn't technically matter what srid you use. However assuming you don't want to throw away that important metadata, and you do want to transform it, you will want to ensure your assigned srid matches the data, so postgis knows what to do when the time comes.
So why would you want to reproject from epsg:4269? The answer is because certain types of queries (such as distance) make no sense in this 'unprojected' world. Your units are in decimal degrees, and a straight measurement of x decimal degrees is a different real distance depending where in the planet you are.
In your example above, someone is using epsg:32661 as they believe it will give them better accuracy for the are they're working in. If your data is in a specific area of the globe, you can select a projection that's accurate for that area. If it spans the entire globe, you have to choose a projection that does 'ok' for your needs.
Now fortunately PostGIS has a few ways of making all this easier. For approx distances you can just use the st_distance_sphere function which, as you might guess, assumes the earth is a sphere. Or the more accurate st_distance_spheroid. Using these, you don't need to reproject and you will probably be fine for your distance queries except in edge cases. Newer versions of PostGIS also let you use geography columns
tl;dr - use st_distance_spheroid for your distance queries, store your data in geography columns, or transform it to a local projection (when storing, or on the fly, depending on your needs).
Take a look at this question: How do you know what SRID to use for a shp file?
The SRID is just a way of storing the WKT inside the database (you may have noticed that, altough you store lat/long points, the preferred storing is a long string with number and capital letters).
The SRID or EPSG can be different for the country/state/... altough there are some very common ones especially the 2 mentioned by you. If you need specific info what area uses what SRID, there is a database for handling that.
Inside your database, you have a table spatial_ref_sys that has the information on what SRID PostGIS knows about.