The Generate Rubbersheet Links, Transfer Attributes, and Detect Feature Changes tools use feature matching techniques to identify corresponding features and produce various results. The feature matching process and feature matching information found in the match table are described below.
Feature matching process
Feature matching means finding corresponding features from two similar datasets based on a search distance. One of the datasets is named source and the other is named target, especially when the feature matching is used to derive rubber sheet links or to transfer attributes from source to target data. These datasets overlap each other but are not perfectly aligned due to inconsistent data collection, changes over time, or other reasons. Figure 1 below shows an example of streets in which the source features come from a commercial data provider and the target features are built and maintained by a city government.
The feature matching process analyzes the source and target topology, detects certain feature patterns, matches the patterns, and matches features within the patterns. The accuracy of feature matching depends on data similarity, complexity, and quality. In general, the more similar the two datasets, the better matching results. Typically, a high percentage of successful matching can be achieved, while uncertainty and errors may occur and require postinspection and corrections.
Feature attributes can help determine the right match in feature matching. If one or more pairs of match fields are specified, spatially matched features are checked using the match fields. For example, if one source feature spatially matches two candidate target features, but one of the target features has matching attribute values and the other doesn't, the former is used as the final match. The condition of attribute match affects the level of confidence of the feature matching.
Match table
The Generate Rubbersheet Links, Transfer Attributes, and Detect Feature Changes tools can produce a match table. The match table provides complete feature matching information with the following fields to help understand the result and to facilitate postinspection and further analysis:
- SRC_FID—The source feature ID. The value is -1 for an unmatched source feature.
- TGT_FID—The target feature ID. The value is -1 for an unmatched target feature.
- FM_GRP—The unique group ID for matched features and -1 for unmatched source or target features
- FM_MN—The match relationships between source and target features in the format of m:n, where m and n are the number of source features and target features in a match group, respectively. For example, 1:1 is a one-to-one match, and 3:2 is a three-to-two match. For unmatched source or target features, the value in this field is N/A for not applicable.
- FM_CONF—This field stores the following values representing the level of confidence of the feature matching:
- 100—Match confirmed by the specified match fields.
- 75—Match not confirmed by the specified match fields, either because match fields are not specified or no values were found in the specified match fields.
- 50—Match with a field value difference in the specified match fields.
- 0—Unmatched source or target features.
The next section has example feature matching scenarios and the matching information in the match tables. For simplicity, an attribute match is assumed for all matched features, reflected in an FM_CONF value of 100.
Match groups and match relationships
Since the feature matching is based on feature topology and spatial patterns in which one or more source features and one or more target features are recognized as having a matching topological structure or spatial pattern, they become a match group. Within each match group, the match relationship is defined by the number of source features (m) versus the number of target features (n), as explained below and illustrated in Figure 2:
- One-to-one (1:1) match
One source feature matches one target feature, and they belong to the same match group.
- One-to-many (1:m) match
One source feature matches multiple target features, and all of them belong to the same match group.
- Many-to-one (m:1) match
Multiple source features match one target feature, and all of them belong to the same match group.
- Many-to-many (m:n) match
Multiple source features match multiple target features, and all of them belong to the same match group.
