Improving a Street-Based Geocoding Algorithm Using Machine Learning Techniques

Abstract

Address matching is a crucial step in geocoding; however, this step forms a bottleneck for geocoding accuracy, as precise input is the biggest challenge for establishing perfect matches. Matches still have to be established despite the inevitability of incorrect address inputs such as misspellings, abbreviations, informal and non-standard names, slangs, or coded terms. Thus, this study suggests an address geocoding system using machine learning to enhance the address matching implemented on street-based addresses. Three different kinds of machine learning methods are tested to find the best method showing the highest accuracy. The performance of address matching using machine learning models is compared to multiple text similarity metrics, which are generally used for the word matching. It was proved that extreme gradient boosting with the optimal hyper-parameters was the best machine learning method with the highest accuracy in the address matching process, and the accuracy of extreme gradient boosting outperformed similarity metrics when using training data or input data. The address matching process using machine learning achieved high accuracy and can be applied to any geocoding systems to precisely convert addresses into geographic coordinates for various research and applications, including car navigation.

Automatic Identification of Addresses: A Systematic Literature Review

“…Address matching is also described as the process of relating the literal description of an address to its corresponding location on a map [4]. In this process, known as geocoding, addresses (up to the street name or street name and door number, combined with a postal code and/or an administrative division) are matched with a reference database in order to obtain the corresponding spatial geographic coordinates [5]. In the absence of a unique identifier (such as the social security number, for instance), addresses can also be used as quasi-identifiers in the linking of records related to the same entity in one or more data collections [6].…”

“…Closely associated to address matching is the task of address parsing or address segmentation, which consists of decomposing an address into its different components, such as a street name or a postal code. Basically, through parsing, it is possible to convert unstructured or semi-structured input addresses into structured ones, helping to overcome imprecise or vague addresses [5].…”

Improving geocoding matching rates of structured addresses in Rio de Janeiro, Brazil

“…Strategies for improving geocoded data often rely on interactive manual processes that can be time-consuming and impractical for large-scale projects. On the other hand, some automated approaches may require large training samples that may not be available in the same language or format as the study addresses 5 .…”

Urban Addressing Practices and Geocoding Algorithm Validity in Developing Countries

“…In the times of IoT (Internet of Things), it's hardly possible today to find data without spatial coordinates. While the latest geocoding literature deals with the latest techniques in the matter, such as machine learning [3] and deep learning particularly [4], the classical issues related to historical address structure and standardization remain relevant [5,6]. The general literature deals with geographic related applications in different countries such as in Australia [7], Brazil [8], China [9], Croatia [10], Cuba [11], Germany [12], India [13], Morocco [14], Quebec [15], South Africa [16], Turkey [17], etc.…”