# Improving a Street-Based Geocoding Algorithm Using Machine Learning Techniques
## 1 Introduction  
Addresses are one of several methods that people perceive location as a textual natural language description. Geographic locations are associated with 80% of the information local government use, and addresses are related to most of the geographic locations [1]. Especially in urban areas, they are used to communicate and reference a spatial location through direct and indirect methods [2, 3]. Since these addresses serve as a link to locate demographic, social, economic, or environmental attributes, Geographic Information System (GIS) proves to be a useful tool across application domains. Integration allows for further analysis and exploring relationships in the context of location, even up to the individual level [4, 5, 6, 7]. Addresses are critical components of geocoding. Geocoding is a process to transform the addresses into geographic coordinates and has become increasingly important in many fields to locate the addresses on a map in GIS. The geocoding was initially developed by the U.S. Bureau of the Census to \"allocate population accurately within blocks, census tracts, and other geographic areas, without the expense of dispatching enumerators to every dwelling unit in the country\" [8]. While Global Positioning System (GPS) may be desirable due to its accuracy, geocoding is regarded as more reliable since GPS may not always be feasible or affordable [9]. Additionally, geocoding supports an expanded view of addresses, including not just structured hierarchical definitions of locations, but also building names, postal codes, and telephone area codes [3]. These days, geocoding has been widely usedin many different fields and research areas, including transportation, public health, and logistics, for mapping not only a particular location itself, but also events and phenomena [10; 11; 12]. Address matching is an essential process in geocoding [13], where input addresses are matched with the counterparts in a reference database to get corresponding coordinates eventually from the matched addresses. This step, however, forms a bottleneck for geocoding accuracy, as precise input is the biggest challenge for establishing perfect matches [14]. Matches still have to be established despite the inevitability of incorrect address inputs [15] such as misspellings, abbreviations, informal and non-standard names, slangs, or coded terms. For example, in the street-addressing system, a street name 'Park Avenue' can have different aliases, such as 'Park Ave.', 'Park Av.'. It can have variants input by different users, and the geocoding system needs to match these aliases with the corresponding counterparts in a reference database. Regarding geocoding, previous studies widely used similarity metrics for matching addresses [16; 17]. Each similarity metric, however, has its characteristic in matching addresses showing different performance in different cases, and we need to find a way to combine such different characteristics for ideally matching addresses correctly. Thus, this study suggests an address geocoding algorithm using machine learning techniques to enhance the address matching implemented on street-based addresses. The developed address geocoding algorithm contains three modules--address parsing, address matching, and address locating. A regex-based parsing method divides the input addresses into elements of the street-based system. For the address matching, this study introduces a way to combine similarity metrics using machine learning to improve performance. We evaluate the suggested address matching, especially the performance of three machine learning models in the address matching process by comparing to multiple text similarity metrics, which are present in previous studies for text matching [18]. This study contributes to the enhancement of geocoding by adopting machine learning techniques for address matching and will be helpful for relevant geocoding research and applications. The sections in this paper are structured as follows: Section 2 describes past studies on geocoding.