| SELECT | |
| osm_id, | |
| feature_type, | |
| osm_timestamp, | |
| geometry, | |
| -- Here we are going to extract a couple of attributes from the all_tags array: | |
| (SELECT value FROM UNNEST(all_tags) WHERE key = 'name' ) AS name, | |
| (SELECT value FROM UNNEST(all_tags) WHERE key = 'addr:city') AS city | |
| FROM | |
| bigquery-public-data.geo_openstreetmap.planet_features |
The absolute easiest way to get a CSV into a postgresql table is to use ogr2ogr with AUTODETECT_TYPE=YES.
I learned a while back that this is what cartoDB uses to import your CSV into postgis (with a lot of other parameters added)
ogr2ogr -f PostgreSQL PG:"host=localhost user=postgres dbname=postgres password=password" docs.csv -oo AUTODETECT_TYPE=YES
| #Requires -RunAsAdministrator | |
| <# | |
| .Synopsis | |
| Download the OSGeo4W installer then download and install QGIS LTR (through the 'full' meta-package). | |
| .DESCRIPTION | |
| This script will: | |
| 1. change the current directory to the user downloads folder | |
| 2. download the OSGeo4W installer | |
| 3. launch it passing command-line parameters to DOWNLOAD packages required to QGIS LTR FULL |
| import boto3 | |
| import pandas as pd | |
| from io import StringIO | |
| class S3DataFrame(pd.DataFrame): | |
| """ | |
| # Make a dataframe and upload it as csv | |
| s3df = S3DataFrame({'h1':[1], 'h2':[2]}) | |
| s3df.to_s3(Bucket='bucket-name', |
| <!DOCTYPE html> | |
| <html> | |
| <head> | |
| <meta charset=utf-8 /> | |
| <title>Leaflet Vector Grid</title> | |
| <meta name='viewport' content='initial-scale=1,maximum-scale=1,user-scalable=no' /> | |
| <link rel="stylesheet" href="https://unpkg.com/[email protected]/dist/leaflet.css" /> | |
| <script src="https://cdnjs.cloudflare.com/ajax/libs/leaflet/1.3.1/leaflet.js"></script> | |
| <script src="https://unpkg.com/leaflet.vectorgrid@latest/dist/Leaflet.VectorGrid.bundled.js"></script> |
| # | |
| apt-get install openjdk-8-jre | |
| # PostgreSQL and PostGIS | |
| apt-get install postgresql postgresql-contrib postgis postgresql-10-postgis-2.4 | |
| # Create "geoserver" database | |
| sudo -u postgres createuser -P geoserver | |
| sudo -u postgres createdb -O geoserver geoserver | |
| sudo -u postgres psql -c "CREATE EXTENSION postgis; CREATE EXTENSION postgis_topology;" geoserver |
| # Count total EBS based storage in AWS | |
| aws ec2 describe-volumes | jq "[.Volumes[].Size] | add" | |
| # Count total EBS storage with a tag filter | |
| aws ec2 describe-volumes --filters "Name=tag:Name,Values=CloudEndure Volume qjenc" | jq "[.Volumes[].Size] | add" | |
| # Describe instances concisely | |
| aws ec2 describe-instances | jq '[.Reservations | .[] | .Instances | .[] | {InstanceId: .InstanceId, State: .State, SubnetId: .SubnetId, VpcId: .VpcId, Name: (.Tags[]|select(.Key=="Name")|.Value)}]' | |
| # Wait until $instance_id is running and then immediately stop it again | |
| aws ec2 wait instance-running --instance-id $instance_id && aws ec2 stop-instances --instance-id $instance_id | |
| # Get 10th instance in the account |
| import json | |
| import geojson | |
| from shapely.geometry import shape | |
| o = { | |
| "coordinates": [[[23.314208, 37.768469], [24.039306, 37.768469], [24.039306, 38.214372], [23.314208, 38.214372], [23.314208, 37.768469]]], | |
| "type": "Polygon" | |
| } | |
| s = json.dumps(o) |
| # extracted from http//www.naturalearthdata.com/download/110m/cultural/ne_110m_admin_0_countries.zip | |
| # under public domain terms | |
| country_bounding_boxes = { | |
| 'AF': ('Afghanistan', (60.5284298033, 29.318572496, 75.1580277851, 38.4862816432)), | |
| 'AO': ('Angola', (11.6400960629, -17.9306364885, 24.0799052263, -4.43802336998)), | |
| 'AL': ('Albania', (19.3044861183, 39.624997667, 21.0200403175, 42.6882473822)), | |
| 'AE': ('United Arab Emirates', (51.5795186705, 22.4969475367, 56.3968473651, 26.055464179)), | |
| 'AR': ('Argentina', (-73.4154357571, -55.25, -53.628348965, -21.8323104794)), | |
| 'AM': ('Armenia', (43.5827458026, 38.7412014837, 46.5057198423, 41.2481285671)), |
| Background | |
| OS VectorMap District is made up of 23 layers of data ranging from woodland to tidal water to POI features. | |
| Ordnance Survey released a number of Style Layer Descriptors (SLDs). These SLDs although they can be imported and used to style the data within QGIS several of the SLDs need editing. There also needs to be some post processing applied to the data in order to render as a cartographic product. | |
| Firstly, I want to use the Dft NUmber and the Road name as a label so I added a new column and created a concatenated string using the SQL commands below. | |
| ALTER TABLE vmd.road ADD COLUMN roadinfo VARCHAR; | |
| COMMIT; |