How do SDIs help improve data understanding through visualization?
Spatial Data Infrastructures (SDIs) and data interoperability are allowing GIS practitioners to easily obtain and use multiple datasets from numerous organizations. The problem is determining if these datasets are useful for your application. If there are multiple sources of the data, which one should the practitioner select for use? Visualizing data is a quick and trusted method of checking out unknown data. Read this blog post to see how data visualization can be used for comparing unfamiliar, voluminous or complex GIS datasets.
Your boss has given you several large geospatial data files and you have been asked to examine the data to see if this data can be used in an upcoming application development project. Without any grand plan to study the data, the first thing you would probably do is visualize it.
When analyzing any unknown data file, why is your first reaction to look at it? Well, the reason is that people acquire more information through vision than all our other senses combined, and we humans have a knack for remembering images rather than numbers or sentences. That’s why we graph spreadsheets and draw maps. Visualization helps us understand complex information.
But how do you do this visualization? Do you start by examining data format specifications and ETL workflow tools, or would you simply load the files into ArcGIS and have a look at it? Most likely you would like to have a look at the data in ArcGIS Pro. A big advantage of ArcGIS Pro is that it has several built-in basemap layers that can be used as reference layers for comparing your data.
Another reason you would examine the data visually is that ArcGIS can read and display assorted data and bring them into a common representation. If reading and visualizing geospatial data was a difficult or time-consuming task, then you might look at doing things differently. However, ArcGIS is very good at loading diverse data formats and data models allowing users to visually compare datasets in one place within minutes.
Here’s an example of some open building data (displayed in red) for Regina, Saskatchewan and visualized in ArcGIS Pro. By performing a quick visual inspection, you can see that there is a slight consistent shift in the building locations from the basemap. Also, there are some buildings in the buildings’ layer that are not in the basemap layer and vice-versa. Finally, some of the open buildings are different shapes than those in the basemap.
SDIs and improving interoperability are allowing users to ingest, visualize and compare multiple layers of data from multiple sources. It’s almost certain that two layers of data from two different sources with completely different provenance will be slightly different. The best way to do an initial inspection of the data is simply to visualize and compare them in ArcGIS. While this visual inspection does not help in comparing the feature attributes, it does give the GIS practitioner a sense of the spatial quality of the two datasets.
Here’s an example of the visualization of four different road network datasets for an area in Ottawa, Ontario. The four road networks are the National Road Network (NRN), the Statistics Canada Road Network File (RNF), the Open Street Map (OSM) roads, and the municipal road network layer from Esri Canada’s GeoFoundation Exchange (GFX). Many of the differences are due to different collection standards; for example, private roads are included in some of the road network layers and not in others. Also, some networks are defined as the road centreline, while others are defined by the driving lanes.
So, data visualization is an important, yet quick and easy, tool that you have in your toolbox for comparing unfamiliar, large or complex datasets. While you won’t get a quantitative assessment value from a visual inspection, you will get a qualitative assessment that can help you decide on your next steps in terms of using the dataset(s) for your application.