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Intelligent Data Conflation

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Intelligent Data Conflation

Overview

GIS data often exists in many forms. The process of combining overlapping spatial datasets to create better datasets with higher accuracy or more information is known as spatial data combining. Conflation is needed in many areas, from transportation planning to historical dataset analysis, all of which require the use of multiple data sources. With the advancement of GIS and the emergence of new sources of spatial data such as voluntary geographic information, geospatial data collation is becoming increasingly important.

Spatial and attribute the difference between data sources may require resolution of discrepancies before the data can be used. Conflation tools help you collate data from multiple sources and get the best data quality possible for analysis and mapping.

Conflation is the process of combining "two digital map files to produce a third map file which is better than each of the component source maps". The datasets in conflation often share many similar features representing the same objects in reality, which need to be matched and merged. Therefore, it is vital in spatial analyses because different agencies and provider, each with a different nature and role, commonly create spatial data for the similar type of objects. Transportation network conflation is a common conflation problem. Many agencies and organizations provide transportation network data due to the importance of roads as movement corridors and a common reference system.

Public agencies, private vendors, and many others often need to combine all sorts of information about transportation infrastructure and the social-economic characteristics of the population from these data sources in their analyses.

The Types Of Problem Encountered In Conflation

The term conflation refers to the difficulty of combining incompatible geospatial data. Other than manual conflation, computerized conflation methods often find potential matches by using certain relationships between candidate features from two datasets. The "cardinality" of relations between entities from relational database theory is an important characterization of the match relation between features. The cardinality of a relation is the number of times entities from one dataset can be linked to entities from another. There are three types of relation cardinality. The first (and most basic) case is the one-to-one matching relationship. This cardinality represents cases where two corresponding features, in reality, correspond to the same object.

The one-to-many matching relation is the second case. This case illustrates how a group of features from one dataset, when combined, represent the same object as a single feature from the other dataset. The many-to-many matching is the third type of cardinality. This includes two-way one-to-many relationships in which one-to-many correspondence exists from dataset 1 to dataset 2 as well as from dataset 2 to dataset 1. Furthermore, the many-to-many case includes more complicated matching in which no feature corresponds to a group of features in the other dataset individually.

After grouping individual features in each dataset, features from the two datasets can only represent the same object in reality. Some conflation algorithms can only handle one-to-one conflation problems, whereas others can handle more complex one-to-many and many-to-many conflation problems.

The Potential Source of Data Conflation

Integrating authoritative and crowdsourced datasets could be one solution to this problem. Data conflation, according to the Open Geospatial Consortium (OGC), is the process of combining two or more separate datasets that share certain characteristics into a single integrated, all-encompassing result.

In a nutshell, data conflation aims to combine geospatial data from disparate sources to create a dataset that is superior to either source alone. Conflation is made up of several sub-processes. The first step entails locating, analyzing, and comparing data to ensure its suitability for further processing. Second, the data must be adjusted to allow for map alignment and spatial or thematic generalization operations.

Conclusion

To achieve unambiguous mapping, features must be matched using geometrical, topological, and semantic attributes at this stage. This is one of the most difficult problems in data conflation because it involves a variety of issues such as different coordinate reference systems, representations, resolutions, or classifications. Once the functions are paired, the required attributes can be joined or transferred between data sets to complete the data conflation process. However, if the process is not properly managed, the data's actual or perceived reliability may be jeopardized.

To better meet the demands of their business and regulatory requirements, corporate and government organizations are increasingly deploying integrated, more sophisticated business and geospatial applications that require higher levels of spatial accuracy. Spatial inaccuracies in geospatial data can severely limit how and when these applications can be used across the enterprise, lowering the value and return to investment (ROI).

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