3.2 Conflation

Conflation, in the context of GIS, is the process of combining two geospatial datasets so that the resultant dataset is superior to the input datasets. While conflation processes are used throughout GIS, they are of particular importance in GIS-T where roadway datasets of varying spatial quality and attribution are available from many different sources. The act of conflating datasets can often be a complex and time-consuming process. How complex and time-consuming the process is depends on a number of factors including the spatial extent of the datasets, the number of features present and the degree of spatial alignment between corresponding features. In some cases, it may be possible to automate a portion of the process but the success of these types of approaches depends on the quality of the initial datasets and the requirements for the final product.

Reference Dataset

When conflating two datasets, one of the datasets is generally considered to be the reference or target dataset. This is the dataset with the most spatially accurate features. The other dataset is sometimes referred to as the input or source dataset.

Conflation Workflow

While each conflation project can be unique, they all draw from a core set of activities. Some of the more common conflation activities include the following:

1. Feature Matching: The objective here is to match corresponding features in the datasets. This process can be based on the spatial alignment of the features and/or certain attributes of the features.
2. Feature Alignment: Once features are matched, they can be brought into spatial alignment with each other to establish proper topological relationships.
3. Feature Addition: Features in the input dataset which are missing in the reference dataset can be added to the reference dataset.
4. Attribute Transfer: Attributes information from the input dataset is added to the reference dataset.

The characteristics of the activities involved in a conflation project are largely dependent on the nature of the input datasets. There are three potentials scenarios:

1. Vector - Vector
2. Vector - Image/Raster
3. Image/Raster - Image/Raster

In GIS-T, we are most commonly engaged in conflating two vector datasets (i.e., roadway data).

Horizontal Conflation vs. Vertical Conflation

Conflation can also be broadly categorized as horizontal conflation or vertical conflation based on the geographic relationship between the datasets. In horizontal conflation, the objective is to join two datasets which are spatially adjacent to each other. For example, perhaps you want to join roadway datasets from two adjacent counties or two adjacent states. In these cases, there is often some feature overlap near the dataset boundaries. In vertical conflation, the datasets being merged span the same geographic region or at least have substantial overlap. The objective is often to transfer a robust set of attribute data from one dataset, which may be of poor spatial accuracy, to a dataset which is poor in attribution but spatially accurate. Of course, in the real world, you may run across situations where the datasets partially overlap.

Conflation Tools

GIS software often has some built-in tools to at least assist with conflation needs. For example, in ArcMap 10.2.1, ESRI introduced a set of tools to help with conflation. The conflation toolset is found in the Editing Toolbox. ESRI also added a tool called Detect Feature Changes to the Data Comparison toolset in the Data Management Toolbox. Spend some time reviewing the help documentation for these tools.