This tutorial shows how to use Google Cloud Platform to build an app that receives telemetric data about geolocation, processes it, and then stores the processed and transformed data for further analysis.

The instructions in this readme show you how to run the tutorial by using Google Cloud Shell with Docker. You can find a version of this tutorial that uses your own development environment, without Docker, on the Google Cloud Platform website.

Cloud Shell provides a ready runtime environment and can save you several steps and some time. However, Cloud Shell times out after 60 minutes of inactivity and can cost you some repeated work, if that happens. For example, anything copied to the /tmp directory will be lost.

Docker provides some automation in deployment that can also make the tutorial easier to run and save you time. However, if you're not familiar with Docker, or simply want to see every step in action, you might prefer to run through the full, manual tutorial steps yourself.

The tutorial:

Starts with traffic data stored in CSV files.
Processes messages in a Google Cloud Pub/Sub queue.
Reverse geocodes latitude and longitude to convert the coordinates to a street address.
Calculates the elevation above sea level.
Converts from Coordinated Universal Time (UTC) to local time by querying which timezone each location is in.
Writes the data, with the added geographic contextual information, to a BigQuery dataset for your analysis.
Visualizes the data as heat maps superimposed over a map of the San Diego metro area.

Costs

This tutorial uses billable components of Google Cloud Platform, including:

1 Compute Engine virtual machine (g1-small)
Google Cloud Storage Standard (5 GB)
Google BigQuery (5 GB storage, 5 GB streaming inserts)
Google Cloud Pub/Sub (< 200k operations)
Google Maps API

The cost of running this tutorial will vary depending on run time. Use the pricing calculator estimate to see a cost estimate based on your projected usage. New Cloud Platform users may be eligible for a free trial.

The Maps API standard plan offers a free quota and pay-as-you-go billing after the quota has been exceeded. If you have an existing license for the Maps API or have a Maps APIs Premium Plan, see the documentation first for some important notes. You can purchase a Maps APIs Premium Plan for higher quotas.

You must have a Maps for Work license for any application that restricts access, such as behind a firewall or on a corporate intranet. For more details about Google Maps API pricing and plans, see the online documentation.

Before you begin

Select or create a Cloud Platform Console project.
Enable billing for your project.
Click the following link to enable the required Cloud Platform APIs. If prompted, be sure to select the project you created in step 1.

Enable APIs

These APIs include:
- BigQuery API
- Pubsub API
- Google Cloud Storage
- Google Maps Geocoding API
- Google Maps Elevation API
- Google Maps Time Zone API
- Google Maps Javascript API

Creating credentials

For this tutorial, you'll need the following credentials:

A Google Maps API server key.
A Maps API browser key.
A credentials file for the service account key.
An OAuth 2.0 client ID.

Google Maps API credentials

If you don't already have them, you'll need Google Maps API keys.

Get a server key

Click the following link to open the Cloud Console in the Credentials page. If you have more than one project, you might be prompted to select a project.

Create credentials
Click Create credentials > API key > Server key.
Name the key "Maps tutorial server key".
Click Create.
Click Ok to dismiss the dialog box that shows you the new key. You can retrieve your keys from the Cloud Console anytime.
Stay on the page.

Get a browser key

The browser key is a requirement for using the Maps Javascript API. Follow these steps:

Click Create credentials and then select API key.
Click Browser key.
Name the key "Maps tutorial browser key".
Click Create.
Click Ok to dismiss the dialog box that shows you the new key.
Stay on the page.

Important: Keep your API keys secure. Publicly exposing your credentials can result in your account being compromised, which could lead to unexpected charges on your account. To keep your API keys secure, follow these best practices.

Service account credentials

Create service account credentials and download the JSON file. Follow these steps:

Click Create credentials and then select Service account key.
Select New service account.
Name the account "Maps tutorial service account".
The key type is JSON.
Click Create.
The Cloud Console automatically downloads to your computer the JSON file that contains the service account key. Note the location of this file.
Click Close to dismiss the dialog box that shows you the new key. If you need to, you can retrieve the key file later.

Upload the JSON file

You must upload the service account credential file to a Google Cloud Storage bucket so that you can transfer it to Cloud Shell in an upcoming step.

In the Cloud Platform Console, go to the Cloud Storage browser.
Click Create bucket.
In the Create bucket dialog, specify the following attributes:
1. A unique bucket name, subject to the bucket name requirements.
2. A storage class.
3. A location where bucket data will be stored.
Click Create.
In the Cloud Console, click the name of your new bucket.
Click Upload files.
Browse to the JSON file and confirm the upload.

OAuth 2.0 client ID

Create a client ID that you can use to authenticate end-user requests to BigQuery. Follow these steps:

Find the IPv4 address of your computer. For example, in your browser, view a page such as http://ip-lookup.net. If your computer is on a corporate intranet, you need to get the address from your operating system. For example, run ifconfing on Linux or ipconfig -all on Windows.
In the Cloud Console, click Create credentials and then select OAuth client ID.
Select Web application.
In the Name field, enter "Maps API client ID".
In the Restrictions section, in Authorized JavaScript origins, add the following two origin URLs. Replace [YOUR_IP_ADDRESS] with the IPv4 address of your computer.

http://[YOUR_IP_ADDRESS]:8000
https://[YOUR_IP_ADDRESS]:8000

Adding these URLs enables an end user to access BigQuery data through JavaScript running in a browser. You need this authorization for an upcoming section of the tutorial, when you display a visualization of data on a map in your web browser.

Click Save to generate the new client ID.

Setting up Cloud Pub/Sub

Cloud Pub/Sub is the messaging queue that handles moving the data from CSV files to BigQuery. You'll need to create a topic, which publishes the messages, and a subscription, which receives the published messages.

Create a Cloud Pub/Sub topic

The topic publishes the messages. Follow these steps to create the topic:

Browse to the Pub/Sub topic list page in the Cloud Console:

Open the Pub/Sub page

Click Create topic. A dialog box opens.
In the Name field, add "traffic" to the end of the path that is provided for you. The path is determined by the system. You can provide only a name for the topic.
Click Create. The dialog box closes.
Stay on the page.

Creating a Cloud Pub/Sub subscription

The subscription receives the published messages. Follow these steps to create the subscription:

In the topic list, in the row that contains the traffic topic, click the downward arrow on the right-hand end of the row.
Click New subscription to open the Create a new subscription page.
In the Subscription name field, add "mysubscription" to the end of the path that is provided for you.
Set the Delivery Type to Pull, if it isn't already set by default.
Click Create.

Preparing to run the code

Follow these steps to prepare to run the code.

Open Cloud Shell. In the Cloud Platform console, in the upper-right corner, click the Activate Google Cloud Shell icon.
In Cloud Shell, clone this repository.
Change directory to resources:

cd geo_bq/resources
Edit setup.yaml. Use your favorite command-line text editor.
For PROJECT_ID, replace your-project-id with your project's ID string. Keep the single quotation marks in this and all other values that you replace.
For DATASET_ID, don't change sandiego_freeways.
For TABLE_ID, don't change geocoded_journeys.
For PUBSUB_TOPIC, replace your-project-id with your project's ID string.
For ROOTDIR, replace the provided path with /tmp/creds/data.
For SUBSCRIPTION, replace your-project-id with your project's ID string.
For MAPS_API_KEY, replace Your-server-key with the server key you created. You can see your credentials by clicking the following link:

View credentials

Save and close the file.
Run the following command:

bash setup.sh

The setup.sh script performs the following steps for you:

Creates a BigQuery dataset and a table schema to receive the traffic data.
Creates a directory structure, /tmp/creds/data, that you use to store your service account credentials (the JSON file you uploaded to your bucket) and the traffic data.
Copies the data files from your GitHub clone to the data directory.
1. Change directory to /tmp/creds:
cd /tmp/creds
1. Copy your credentials file. Run the following command. Replace [YOUR_BUCKET] with the name of your Cloud Storage bucket and [YOUR_CREDENTIALS_FILE] with the name of the file:

gsutil cp gs://[YOUR_BUCKET]/[YOUR_CREDENTIALS_FILE].json .

Pushing data to Cloud Pub/Sub

Next, run the code to push the traffic data to Cloud Pub/Sub. Run the following command. Replace [YOUR_CREDENTIALS_FILE] with the name of the file.

docker run -e "GOOGLE_APPLICATION_CREDENTIALS=/tmp/creds/[YOUR_CREDENTIALS_FILE].json" --name map-push -v /tmp/creds:/tmp/creds gcr.io/cloud-solutions-images/map-pushapp

After Docker finishes initializing, you should see repeated lines of output like this one:

Vehicle ID: 1005, location: 33.2354833333, -117.387343333; speed: 44.698 mph,
bearing: 223.810 degrees

It can take some time to push all the data to Pub/Sub.

Loading the data into BigQuery

BigQuery pulls the data by using the Cloud Pub/Sub subscription. To get it going, run the following command. Replace [YOUR_CREDENTIALS_FILE] with the name of the file.

docker run -e "GOOGLE_APPLICATION_CREDENTIALS=/tmp/creds/[YOUR_CREDENTIALS_FILE].json" --name map-app -v /tmp/creds:/tmp/creds gcr.io/cloud-solutions-images/map-pullapp

After Docker finishes initializing, you should see repeated lines of output like this one:

Appended one row to BigQuery. Address: 11th St, Oceanside, CA 92058, USA

It can take some to pull all the data from the topic. When it’s done, the terminal window will stop showing lines of output as it waits for further data. You can exit the process at any time by pressing Control+C. If Cloud Shell times out, you can simply click the reconnect link.

Analyzing the data

Now that the you have transcoded and loaded the data into BigQuery, you can use BigQuery to gain insights. This section of the tutorial shows you how to use the BigQuery console run a few simple queries against this data.

Open the BigQuery Console.
Select the sandiego_freeways database.
Click the Compose Query button.
In the New Query text box, enter the following query that produces average speed by zip code:

SELECT AVG(Speed) avg_speed, Zipcode FROM [sandiego_freeways.geocoded_journeys] 
WHERE Zipcode &lt;> ''
GROUP BY Zipcode ORDER BY avg_speed DESC

Visualizing the data

You can use Google Maps to visualize the data you stored in BigQuery. This tutorial shows you how to superimpose a heat map visualization onto a map of the region. The heat map shows the volume of traffic activity captured in the data in BigQuery.

To keep the tutorial straightforward, the provided example uses OAuth 2.0 to authenticate the user for the BigQuery service. You could choose another approach that might be better-suited for your scenario. For example, you could export query results from BigQuery and create a static map layer that doesn’t require the end user to authenticate against BigQuery, or you could set up authentication by using a service account and a proxy server.

To show the data visualization, follow these steps.

Modify `bqapi.html`

For these modifications, you need to use keys and credentials you created earlier. You can see these values in the Cloud Console on the Credentials page.

Make a copy of the file named bqapi.html. You can find the file in the following directory where you installed the source code:

geo_bq/web/

Open the file in a text editor.
In the following script element, in the src attribute, replace Your-Maps-API-Key with your Google Maps API browser key:

&lt;script src="<a href="https://maps.googleapis.com/maps/api/js?libraries=visualization,drawing&key=Your-Maps-Api_browser-key">https://maps.googleapis.com/maps/api/js?libraries=visualization,drawing&key=</a>Your-Maps-API-Key"

For the clientId variable, replace Your-Client-ID with the OAuth 2.0 client ID you created earlier.
For the projectId variable, replace the value string, Your-Project-ID, with the your project ID.
Save the file.

Viewing the web page

You can use Cloud Shell to view the web page. Follow these steps:

From the geo_bq/web directory, run the following command to start the server:

python -m SimpleHTTPServer 8080

When the server is running, Cloud Shell prints the following message:

Serving HTTP on 0.0.0.0 port 8080 ...
In the top-left corner of Cloud Shell, click Web preview and then click Preview on port 8080. Cloud Shell opens a new browser tab that connects to the web server.
In the new browser tab, note the origin URL. The origin URL has a format similar to the following example, where [RANDOM_NUMBER] could be any value:

https://8080-dot-[RANDOM\_NUMBER]-dot-devshell.appspot.com
In the Cloud Console, return to the Credentials page;
Click the name of your OAuth 2.0 client ID.
In the Restrictions section, add the origin URL you noted in the previous step. Do not add a port number.

The origin URL that you provide in this step tells OAuth 2.0 that it's safe to accept requests from the Cloud Shell browser. Without this step, the web page can't use script to access the data you loaded into BigQuery.
Click Save.
In the browser tab that Cloud Shell opened, click the link for bqapi.html. If your browser has a pop-up blocker, turn it off for this site.
In the pop-up window, follow the OAuth 2.0 authentication prompts. You won't have to repeat this flow in this session if, for example, you reload the web page.
After the map has loaded, select the rectangle tool in the upper-left corner of the map.
Use the tool to draw a rectangle around the entire currently visible land mass on the map.

The page shows a heat map. Exactly where the heat map regions display on the map depends on the data you loaded into BigQuery.

For details about how the code works, see the tutorial on the Google Cloud Platform site.