Data Engineering is a new addition to Pro – Yuwei takes us on a tour.

Analysis and mapping with ArcGIS Pro rely on data of good quality. In the broader sense, data engineering in ArcGIS involves data extraction, cleaning, location enabling, enriching, and transformation.  Oftentimes many of these tasks can be done in spreadsheets such as Excel, or through computer programming. However, going back and forth between ArcGIS and spreadsheets may be less efficient than desired. Starting with Version 2.8, ArcGIS Pro comes with a Data Engineering view to facilitate data preparation within ArcGIS. The view is compatible with feature layers and standalone tables. This integrated approach allows fast data exploration with statistics, plots and mapping thus can make data preparation more efficient.

In this blog, I will use the Data Engineering view to show some of its main features with a feature layer.

Assume that I want to do some mapping with regard to business and employment in the Metropolitan Christchurch Area. I downloaded a dataset on NZ Business Demography Statistics at February 2000 at the SA2 level from Stats NZ (https://datafinder.stats.govt.nz/layer/105388-new-zealand-business-demography-statistics-at-february-2020-on-statistical-area-2-2020/), and already extracted data on SA2s within the 4 districts (Christchurch, Selwyn, Waimakariri, Hurunui) through some geoprocessing in ArcGIS. After adding the resulting feature layer to the map, clicking the layer in the Contents pane activates the top-level menu items under the group Feature Layer. Clicking on the Data item shows a ribbon on which Data Engineering can be clicked next.  Alternatively, right clicking the layer in the pane then click Data Engineering. This opens the Data Engineering view.

A contextual Data Engineering menu also shows up.

As usual, we can examine the data by looking at the Attribute Table or view the Fields. But with Data Engineering view, we can also explore the data contained in each field. Hover over a field, and two coloured icon shows up.

Clicking the Update Symbology icon allows mapping based on the selected field. This can be handy when the feature layer contains many fields of interest. The mappings below shows the number of employees and businesses respectively. Notably for the Hagley Park SA2, the number of businesses is small but the number of employees is large. In this case we are looking at the Christchurch hospitals.

If we click the Create Chart icon on Total_employee_count, we get a bar chart showing the distribution of Total_employee_count. This chart clearly shows the effect of zoning.

If we drag a field into the Data Engineering view main window, we can then click Calculate to further explore statistical data associated with the field.

Additional main feature sets under Data Engineering include: Clean, Contruct, Integrate, and Format. These functionalities are shown below and can be further explored by the user.