Google tools for GIS Applications

Understand how cloud database servers enable multi-user access and real-time updates for geospatial data, and why they outperform file storage through client-server architecture and access control.

Learn to spin up a PostgreSQL database with Posteriors on Google Cloud, configure a single-zone instance, and connect using PGAdmin 4 for GIS-ready spatial data.

Explore BigQuery, a Google Cloud data warehouse for petabyte-scale data, offering distributed queries, geospatial support, and Google Data Studio integration, with limited Postgres compatibility and first terabyte free.

Load tables in BigQuery, join them with Postgres data on habitat ID, using aliases, where clauses, and order by to illustrate one-to-many spatial joins.

Learn to join spatial data from Postgres and BigQuery using geography and distance functions to identify projects within 500 meters of active nests.

Compare geometry and geography data types in Postgres and BigQuery, explain when to use each, and discuss planar versus spherical distances, bounding boxes, and spatial indexing for performance.

Evaluate performance of GIS queries in Google tools by benchmarking BigQuery and Cloud SQL, comparing geometry and geography, and applying bounding box filtering to accelerate spatial joins.

Learn to use Google Cloud Shell to run BigQuery commands, create and replace tables, and add a geography column while automating data updates from external connections.

Transform commands from the cloud shell editor into a runnable shell script, chmod it, and execute to update geography and import raptor, eagle, and burrowing owl data into BigQuery.

Create a unified dashboard in Google Data Studio by combining charts, tables, and maps from a BigQuery data source using connectors. Apply a theme and layout to organize it.

Add interactive data controls in Google Data Studio to filter maps and charts by habitat id, project, or status, using dropdowns, sliders, and date ranges.

Improve the Google tools for GIS applications dashboard by renaming data sources, adjusting per-page controls, and adding calculated fields like type_short to display per-project details clearly.

Use a case statement in data studio to standardize type values into categories like access road and pipeline. Create calculated fields and apply status as color to clarify maps.

Create and rename data sources in data studio, pull Burning Owl survey records from BigQuery and an external Postgres connection, and switch between production and working data every 15 minutes.

Add a second page to the report, rename pages, set a default BigQuery data source, and configure a summary table with habitat and survey date plus habitat and surveyor filters.

Copy the original page, switch to a raptor data source in BigQuery, and tailor the map with species-based buffer distances and updated geography fields for raptor impacts.

Learn to share Google data studio dashboards with clients, set viewer or editor permissions, and protect production data while managing data sources, copies, and visibility.

Combine raptor and eagle and other constraint data into a single constraint table by unifying fields, adding constraint identifiers, and converting point locations to polygons for a unified gis view.

Explore how to create and manage Google Cloud Storage buckets, choose regional storage options, set access controls, and understand versioning and cost differences to organize and share GIS data.

Learn to make cloud storage data publicly accessible by adjusting object ACLs or using a bucket with uniform access, enabling public downloads and browser viewing.

Use gsutil to manage cloud storage from the command line, copying files, setting permissions, and handling buckets with cp, mv, mb, and rm; leverage wildcards for bulk transfers.

Learn how to back up BigQuery and Cloud SQL data to Cloud Storage using shell commands, including creating a regional archive bucket, selecting storage classes, and configuring IAM permissions.

Install and configure the Google Cloud SDK (gcloud) on your local computer to manage cloud storage and run BigQuery and other cloud commands from the terminal.

Discover Google Colabs, cloud-hosted Jupyter notebooks in Google Cloud that integrate with BigQuery, Cloud Storage, and Drive, enabling shareable, collaborative data work with Python.

Access geospatial data from public and private Google Cloud Storage buckets via notebooks, authenticate users, manage authorization, and read shapefiles into a dataframe.

Load data from a Google Cloud SQL database into a geopandas GeoDataFrame via a connection object, then visualize spatial data and color-code by a geometry-bearing column.