![]() More columns also required adding to the GROUP BY portion of the query. I ran across this while trying to perform a similar task with a query containing about a dozen columns. OpenStreetMap represents physical features on the ground (e.g., roads or buildings) using tags attached to its basic data structures (its nodes, ways, and relations).Each tag describes a geographic attribute of the feature being shown by that specific node, way or relation. I do believe my approach is a bit easier to follow. Each geolocation service you might use, such as Google Maps, Bing Maps, or Nominatim, has its own class in geopy.geocoders abstracting the service’s API. That is still significantly slower then the other two queries. Adding a key to the user_id on the posts and pages tables avoids the file sort and sped up the slow query to only take 18 seconds. Using EXPLAIN with each of the queries shows that both of your approaches involves a filesort which is avoided with my query. Your updated simpler method took over 2000 times as long (nearly 3 minutes compared to. Limited testing showed nearly identical performance with this query to your query using left join to select subqueries. To test performance differences, I loaded the tables with 16,000 posts and nearly 25,000 pages. (select count(*) from pages where er_id=er_id) as page_count (select count(*) from posts where er_id=er_id) as post_count, If OGR driver is implemented, the primary source should be probably reading from an URL via curl. My solution involves the use of dependent subqueries. standard web server - exactly like GDAL2Tiles or does by default for raster tiles. INSERT INTO users (name) VALUES ( 'Jen ') ĬREATE TABLE posts (post_id INT PRIMARY KEY AUTO_INCREMENT, user_id INT) ĬREATE TABLE pages (page_id INT PRIMARY KEY AUTO_INCREMENT, user_id INT) INSERT INTO users (name) VALUES ( 'Simon ') INSERT INTO users (name) VALUES ( 'Matt ') Should have some useful code, I think.CREATE TABLE users (user_id INT PRIMARY KEY AUTO_INCREMENT, name VARCHAR( 20)) The “main” version technically lives inside of GDAL’s codebase at, although it’s apparently not been maintained much. There may also be some relevant code in the gdal2tiles.py open-source script, of which there are a gazillion forks floating around. You may also want to glance in the Cesium source at /Source/Core/GeographicTilingScheme.js. Uh… some relevant keywords to look for are “EPSG:4326”, “Plate Carree”, and “2x1”, and some related links are at, ,, , and. Google apparently uses 1x1 at level 0, but 2x1 is another frequently used approach. Our extremely fast Cloudpush technology can transfer the data to and from various cloud storage services like. Our engineers can deploy the system on any other public or private cloud platform on request. You can try it for free in marketplace and buy the full license there. I can tell you that there’s some variations on tiling schemes out there. MapTiler Cluster is currently available on Google Cloud Platform. Not sure if I have a specific answer for you. After it is finished a new section is added to Start -> All Programs -> MapTiler Desktop Pro, and it is possible to use the command maptiler from the standard Command Prompt application in Windows. joining dozens of individually requested image or vector data files. So can anyone help me figure out how to calculate a Lat/Long bounding box for the tiles requested by Cesium? A tiled web map, slippy map (in OpenStreetMap terminology) or tile map is a map displayed in. So I am now assuming I'm just using the wrong projection to calculate the tile bounds.Ĭonverting from pixes, to meters, to lat/long but they seem to be the same results as the web mercator calculations. If I plot my generated lat, long point on a 2D Google Map, they look correct, but I can clearly see that the tiles requested by cesium have different bounds. That said, my calculations seem to work for zoom levels 0,1,2, and 3, but as I go to Zoom level 4, the Latitude calculation starts to move south. That's not what I see with Cesium (there are 2 tiles at zoom level 0). I'm using the same tile scheme as Google Maps with a request like Initially I've been using the equations to convert these map tile coordinates into lat/long bounds found here: but now I'm wondering if this is not the correct projection to be using.įor starters, all the Web Mercator documentation I see says that Tile 0,0 zoom lvl 0 should cover the entire world. For each map tile, I need to know the lat/long bounds in order to find matching data. ![]() ![]() I am trying to generate map tiles based on data we have stored in a DB and serve them up to Cesium using an UrlTemplateImageryProvider. Auxiliary aids and services are available upon request to individuals with disabilities.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |