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# How to Seamlessly Include Geospatial Data Operations for Data Integration

### The demand for having spatial information in data-driven decision-making processes has reached a new high. You can enrich your ETL processes with spatial data.

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Hortonworks Sandbox for HDP and HDF is your chance to get started on learning, developing, testing and trying out new features. Each download comes preconfigured with interactive tutorials, sample data and developments from the Apache community.

With the increased availability of data through sensors, interconnected mobile devices, social media, and private or public spatial data sets, the demand for the seamless integration of spatial information into data-driven decision-making processes has reached a new high. We consider spatial data as any kind of data supplemented with additional information about the location and shape of objects on earth. One simple example would be the general information of companies, buildings, persons, and/or vehicles such as name, type, and color, with supplementary X, Y- coordinate values defining their current or permanent position on the earth respectively. But real-world objects often have much more complex forms. For example, using line-geometries with several nodes, we can describe a street, a river, or even movements of a car or a person. With polygon geometries, we can additionally describe settlements, shops, forest areas, etc. Spatial data with their diversity of formats and spatial reference systems and their specific geometrical operations have been historically managed separately while being only occasionally if at all, considered in traditional data integration processes.

To demonstrate the functionality, let’s have a look at the following small scenario with wind farms. The individual turbines of the wind farms are available as single location objects (points) and we would like to calculate the full area of the wind farm (or to speak in geometrical terms, to calculate the convex hull of all points belonging to one wind farm location and hence grouped by an attribute).

This exercise includes the following steps:

1. Define the metadata for our new data source (shape file).
2. Define the Talend job to calculate the wind farm area by applying a geometrical operation.
3. Execute the job and write the resulting areas in a file or database.

This simple process allows users to define and visualize the wind farms’ expansion area, where the group of wind turbines is located, and thus create the basis for further planning steps in the fields of road construction or nature conservation.

## 1. Define the Metadata to Load the Geometries From a File or Database

First, we have to register the new data source we want to read. In our case, it is the shape file with the wind turbine location information. If you have spatial data in another format or spatial database like Oracle Spatial or PostgreSQL with PostGIS, the process is very similar.

This is how the sample file with the wind turbines looks like if visualized as individual point objects. For the purpose of demonstration, our GIS and Reporting platform Cadenza Desktop is used. The background map is based on Open Street Map.

Next, we have to define the metadata for our new data source in Talend as follows. This way, we can use this data source for the example-job and any other future jobs.

1. Specify the name and purpose.
2. Select a path to the source file with the wind turbines. If the shape file has a project-file (`.prj`) the coordinate reference system is automatically identified and assigned. In other cases, you can enter it manually.
3. Edit the schema.

Schematic diagram data such as column names and data types are imported automatically. Adjustments can be made if required (for example, setting the primary key, if this has not been specified in the data records).

The shapefile has now been included in the Talend metadata and can be used for the next step or for any other job you want to start with this data. After this step, the metadata-tree should look like this in Talend Open Studio:

## 2. Create a Talend Job Including the Spatial Operators

In this step, our aim is to generate a geometry that covers all wind turbines and to create a convex hull around all wind turbines using the geometry functions as illustrated below:

To do so, you have to create a new job in Talend and enter a name, purpose, and description for it. Then drag the new data source, “windfarm” (created in the previous step), and drop it to the job’s workspace. A dialog window will open automatically. You can now choose a component you wish to use. Since we are working with a shape file, select dShapeFileInput.

The shape file with our wind turbines is now ready to be further processed. Since we want to calculate the convex hull (wind park area) of our points, select the new dAggregateGeometryRow component, move it to the job’s workspace, and choose the main-row connection between the dShapeInput and dAggregateGeometryRow components.

Finally, we have to define the output. In our case, the output is a simple shape-file containing just the name of the wind park and the new geometries created. Select the dAggregateGeometryRow component and navigate to the Component tab. Click the EditSchema button in order to define the component’s output schema. By clicking the Add button (“+” symbol), we create two new columns: „Name“ ( Type: STRING, Length: 80) and “ConvexHull“ (Type: DGeometry ).

As our source file contains points from different wind parks and we want to generate a convex hull from wind turbines pertaining to each wind park, the wind turbine points need to be grouped by the name of each wind park. This can be accomplished by setting the Group by property of the component dAggregateGeometryRow_1 to the NAME column (refer to the illustration below).

Add a tMap component to the workspace and create a main-row link between the dAggregateGeometryRow and the tMap component as follows:

Open the tMap component by double-clicking it, create a new output data stream, and call it, for instance, "out1".

Now we can map the input data stream via drag-and-drop to the output data stream as shown below. It is recommended that you create a unique identifier for all output data sets. In our example, we hence create a new ID column for the unique identifier as an Integer data type by using the Numeric.sequence(“s1”,1,1) function:

The newly created spatial and attribute data shall be saved as a shapefile. For this, we can use the new dShapefileOutput component as follows.

To customize the output stream, navigate to Component properties and select the storage location of the shapefile. Also, select the same coordinate reference system as defined in the data source. In our example, it is EPSG:3857 (AKA WGS84 or Web Mercator).

## 3. Execute the Job and Write the Resulting Areas in a File or Database

That’s it! Now you can run the job and the output files will be saved to the file you specified for further use. After the execution of your job, your ETL process should now look like this:

Congratulations! You just processed your first spatial ETL job without leaving Talend Open Studio.

For the purpose of demonstration of the result, the screenshot below shows the newly created areas added in our software Cadenza:

As you can see, the new areas (the wind parks) are created including all single wind turbines of an area.

## More Steps With Spatial Data

If you start thinking about it, you are sure to find many other examples where spatial information and operations will be valuable for your business decision. Maybe you would like to:

• Calculate the best location of you next shop based on demographic data and driving distances to your current shops.

• Analyze the location of mobile website visitors to trigger special location-based offers.

• Identify new areas where offline marketing would have the best impact on your target audience.

• Extend your regular reports by additional spatial information, provide deeper spatial analytics and location intelligence to your processes, etc.

After downloading the plugin for GeoSpatial Integration, you will find many additional samples and we will send you further click-through tutorials to experiment with Spatial-ETL. Do try it out!

Let us know what you think in the comments below and become spatial data-driven.

Hortonworks Sandbox for HDP and HDF is your chance to get started on learning, developing, testing and trying out new features. Each download comes preconfigured with interactive tutorials, sample data and developments from the Apache community.

Topics:
big data ,spatial data ,data integration

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