This should be based on the resulting structure of the geocoding API. After creating the output classes, we can now call the API using the key and coordinate. The reverse geocoding will return the nearby address to the coordinates we have provided with the nearest one first. Here we are only printing the formatted address. You can manipulate the results as your need as the result contains each component along with the formatted address. Reverse geocoding is a critical component of location-based services with a wide range of application areas.
Apart from latitude and longitude, the function incorporates an optional third parameter ResultTypeFilter. In the code comments, you will find the values that are supported.
Bear in mind that the use of the Google Geocoding API is subject to a limit of 40, requests per month, so be careful not to exceed this limit. Check this link that presents a step-by-step guide on how to acquire one for free. NOTE: In case of multiple results, the function returns the first result , so be careful with your inputs. Tip: use the ResultTypeFilter parameter to reduce the results that are returned.
The file can be opened with Excel or newer. Please enable macros before using it. Geocoding means converting addresses or location names into geographic coordinates or x- and y-coordinates that can be plotted on a map, as shown in Figure 2.
The first number is the latitude and the second is the longitude. Since the equator is 0 degrees latitude, positive latitude is the northern hemisphere, and negative latitude is in the southern hemisphere. Similarly, the prime meridian is 0 degrees longitude, which passes through Greenwich, England. So positive longitude is east of the meridian, and negative longitude is west, until you reach the opposite side of the globe, roughly near the International Date Line in the Pacific Ocean.
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 Google Cloud users might 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.
You must have a Google Maps license for any app that restricts access, such as behind a firewall or on a corporate intranet. For more details about Google Maps Platform pricing and plans, see the online documentation. Select or create a Google Cloud Console project. If prompted, be sure to select the project you created in step 1. Enable APIs. Install the Cloud SDK. Set up your Python development environment.
Follow these instructions to install on Linux , macOS , or Windows. Download the code and other files to your local computer. To clone the GitHub repository by using git :. Alternatively, you can download and unzip the archive. Change to the resources directory from the directory where you downloaded the code:. Create a client ID that you can use to authenticate user requests to BigQuery.
Follow these steps:. Click the following button to open the Credentials page in the Cloud Console. If you have more than one project, you might be prompted to select a project. Create credentials. 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.
In the Accept requests from these server IP addresses field, enter the IPv4 address of you computer, which you noted in the previous section. You need to create a topic , which publishes the messages, and a subscription , which receives the published messages.
Add traffic to the end of the path that is provided for you in the Name field. The path is determined by the system. You can provide only the name for the topic.
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.
Add mysubscription to the end of the path that is provided for you in the Subscription name field. To prepare BigQuery to receive the data that you want to analyze, you must create a dataset , which is the logical container for the data tables, and then add a table to the dataset, which specifies the schema and stores the data in the specified format.
BigQuery console. Near the top of the left-hand panel, locate your project name. Now you can perform the steps that import the data from the CSV files, transcode the data, and load it into a BigQuery table. From the resources directory, use your preferred text editor to edit setup. Keep the single quotation marks in this and all other values that you replace. You can see your credentials by clicking the following button:.
View credentials. This output confirms that each line of data has been pushed. If you don't see many lines of such output, double-check setup. For each line in the CSV file, the script performs a basic conversion on the latitude and longitude values to format them in units of degrees. To pull the data from the topic and load it into your BigQuery table, you run another Python script.
This script uses the same setup. It can take some time 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. The code also sets up some configuration values, such as the size of a message batch and some limits for geocoding operations to stay within daily quotas. The following code also retrieves the name of the subscription from the setup.
The code processes each message. The key point here is that the code uses the Google Maps Platform to reverse geocode from latitude and longitude to street address. Click View on GitHub to see the rest of the data extraction and transcoding of the data format for storage in BigQuery.
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 to run a few simple queries against this data. BigQuery Console. In the New Query text box, enter the following query that produces average speed by zip code:. You can use Google Maps to visualize the data you stored in BigQuery.
This part of the 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.
You could choose another approach that might be better-suited for your scenario. The file named bqapi. It requires some changes to work with your data. 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. Open Credentials. Make a copy of the file named bqapi. You can find the file in the following directory where you installed the source code:.
If your browser has a pop-up blocker, you must disable it for traffic on localhost and then refresh the page. Click Allow in the OAuth 2. You won't have to repeat this flow in this session if, for example, you reload the web page. The page shows a heat map, similar to the following map.
Exactly where the heat map regions display on the map depends on the data you loaded into BigQuery. You saw how the page sets up some of the configuration, such as how it references the visualization library when you added your browser key.
In this section, you take a deeper look at how the page authorizes the user, retrieves data, and renders the heat-map regions. The following functions handle authentication and authorization through OAuth 2. The function named authorise makes the request for authorization. The function named handleAuthResult receives a callback from the OAuth 2. Recall that you draw a rectangle around the region of the map where you want to see the heat maps.
The rectangle you draw defines a set of coordinate boundaries that restrict the subset of data to retrieve from BigQuery. The function named setUpDrawingTools adds an event listener that notifies your code when the rectangle is drawn. You can see that this function uses southwest and northeast corners of the rectangle to define the boundaries for latitudes and longitudes in the dataset.
For example, the longitude represented by sw. Any longitude in the dataset that is greater this value would be to the right of the that edge, and therefore inside the boundaries of the rectangle. Similar logic applies to the other three sides of the rectangle. The checkJobStatus function shows how to check the status of a job periodically, calling the get method with the jobId returned by the original query request.
The function in the sample uses a millisecond timeout. The checkJobStatus function calls getQueryResults. This function uses the jobs. It's important to understand that the amount of data that can be returned from a BigQuery dataset can be huge, sometimes amounting to petabytes of data.
You must be careful to aggregate such data in a way that makes it usable so that it can be processed and displayed in a reasonable amount of time. For example, trying to plot the location of every row of traffic data would be untenable in this scenario.
Fortunately, the Maps API provides the visualization. HeatmapLayer object, which is well-suited for this purpose.
The doHeatMap function creates the heat map and then superimposes the visualization onto the map that is displayed in the browser. BigQuery scans the entire table with every query.
To optimize your BigQuery quota usage, select only the columns you need in your query. Queries run faster if you store latitude and longitude as float rather than string. There are other ways to use SQL to run spatial queries against data in BigQuery, including queries that approximate a bounding circle, and User Defined Functions that can be used to construct more-advanced geometry operations. There are examples of bounding-box and circle-radius queries in the Advanced Examples section of the BigQuery reference.
After you finish the tutorial, you can clean up the resources that you created so that they stop using quota and incurring charges. The following sections describe how to delete or turn off these resources.
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