A Role based Address Cleaner

Computers, Environment and Urban Systems, 2018

Geocoding is a tool that can be used in many areas such as the development of disaster prevention systems, crime mapping and the monitoring of communicable diseases, and which has gradually gained importance. However, the use of geocoding is not yet possible in some areas where it could serve as an effective tool, for various reasons such as inconsistencies in address formats, including inaccurate numbering systems, misspellings, the use of abbreviations and a lack of data that refers to the geocoding process. This study seeks to address these problems by way of a standardization process. To that end, it employs a method that decomposes addresses used as input data in geocoding, identifies spelling mistakes and abbreviations, and reorganizes the addresses through the Natural Language Process (NLP). As test data, the addresses of primary schools in the district of Eskisehir are taken. First the geocoding process is performed on the data set, using both Google geocoding API and ArcGIS geocoding API. Then, the addresses are reformatted into three address formats by applying standardization processes. Geocoding is performed on the re-formatted addresses and the results compared to the non-standardized results. The standardization used is shown to make a significant improvement in the accuracy of the geocoding results. The method used in this study is significant not only in increasing the accuracy of the geocoding process, but also in sustaining its wider use.

IRJET, 2023

Address validation holds fundamental value in confirming the accuracy and geographical precision of addresses used by location-dependent and delivery-based organizations. Addresses often suffer from problems such as missing components and geographical inadequacies which can cause grave logistical issues if not validated adequately beforehand. The identification of missing or invalid address components to perform address validation can prove to be a helpful factor in saving time and cost for businesses and simultaneously reducing the chances of errors in service. Significant potential has been found in the usage of statistical measures such as correlation coefficients and measures of central tendency to perform the task of address validation. The system proposed in this paper uses a combination of different string-matching metrics to generate a normalized score based on statistical similarities. This score can then be used to filter out validated addresses according to the threshold of similarity required. Experiments have been conducted on a real-world healthcare dataset to demonstrate the effectiveness of the proposed approach in terms of accuracy and precision.

Data mining has become an important task of today's rich information environments. Strong results are obtained through accurate historical reporting. Inaccurate and dirty records yield weak and wrong analysis. Unfortunately, organizations store addresses in unstructured formats resulting in multiple representations of same entities. These addresses need to be cleansed before they can be used in mining data. In this paper we present a supervised machine learning procedure, Hidden Markov Model (HMM). This automated probabilistic approach is used to segment a set of Asian addresses into their atomic units and standardize them. Results of this technique show that it can also be used to standardize even a large set of complex and un-formatted addresses.

The "United States Street, Landmark, and Postal Address Data Standard" is a draft data standard for United States address information. The draft standard defines and specifies elements and structures for organizing address data, defines tests of address data quality, and facilitates address data exchange. The draft standard has four parts: Data Content, Data Classification, Data Quality, and Data Exchange.

The original purpose of addresses was to enable the correct and unambiguous delivery of postal mail. The advent of computers and more specifically geographic information systems (GIS) opened up a whole new range of possibilities for the use of addresses, such as routing and vehicle navigation, spatial demographic analysis, geo-marketing, and service placement and delivery. Such functionality requires a database which can store spatial data effectively. In this paper we present address databases and motivate the need for national address databases. We describe models used for national address databases, and present our evaluation framework for an address database at a national level within the context of a spatial data infrastructure (SDI). The models of data harvesting, federated databases and data grids are analyzed and evaluated according to our novel framework, and we show that the data grid model has some unique features that make it attractive for a national address database in an environment where centralized control and/or coordination is difficult or undesirable.

ISPRS International Journal of Geo-Information, 2021

Address matching continues to play a central role at various levels, through geocoding and data integration from different sources, with a view to promote activities such as urban planning, location-based services, and the construction of databases like those used in census operations. However, the task of address matching continues to face several challenges, such as non-standard or incomplete address records or addresses written in more complex languages. In order to better understand how current limitations can be overcome, this paper conducted a systematic literature review focused on automated approaches to address matching and their evolution across time. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed, resulting in a final set of 41 papers published between 2002 and 2021, the great majority of which are after 2017, with Chinese authors leading the way. The main findings revealed a consistent move from more traditional a...

2008

This paper presents FuMaS (Fuzzy Matching System), a system capable of an efficient retrieval of postal addresses from noisy queries. The fuzzy postal addresses retrieval has many possible applications, ranging from datawarehouse dedumping, to the correction of input forms, or the integration within online street directories, etc. This paper presents the system architecture along with a series of experiments performed using FuMaS. The experimental results show that FuMaS is a very useful system when retrieving noisy postal addresses, being able to retrieve almost 85% of the total ones. This represents an improvement of the 15% when comparing with other systems tested in this set of experiments.

2019

We have developed a comprehensive prototype solution for a specific use case involving entity resolution for mailing addresses of financial institutions. Our objective was to find matches between user entry of misspelled or inaccurate addresses of business entities and their corresponding entries in a “gold copy” of complete and accurate mailing addresses (dictionary). Three distinct matching methods (PySolr, SoDA and Record Linkage) were used for a preliminary, yet diverse scheme of lookups in finding matches. These lookup processes may optionally be followed by search via a hybrid machine learning (ML) model via regularized logistic regression and hierarchical clustering using Dedupe. Our experimental results of elapsed times for searches using the three lookup methods on a variety of match types suggest that majority of the simpler matches are detected extremely fast (elapsed times: ~ 6 – 48 milliseconds) at the lookup stage, making it suitable for detecting simple and possibly m...

A lot of research works have been devoted to geocoding of postal addresses. The interest in this topic is supported by the need to transform postal addresses into geographical coordinates which are essential for various domains of scientific and social research. The benefits of the address geocoding precision are numerous. Geocoding can be used for a wide range of applications such as market segmentation, demographics, geo-spatial distribution of plants, sales territories, taxes, elections. Geocoding is also a very important ...

The widespread availability of powerful geocoding tools in commercial GIS software and the interest in spatial analysis at the individual level have made address geocoding a widely employed technique in many different fields. The most commonly used approach to geocoding employs a street network data model, in which addresses are placed along a street segment based on a linear interpolation of the location of the street number within an address range. Several alternatives have emerged, including the use of address points and parcels, but these have not received widespread attention in the literature. This paper reviews the foundation of geocoding and presents a framework for evaluating geocoding quality based on completeness, positional accuracy and repeatability. Geocoding quality was compared using three address data models: address points, parcels and street networks. The empirical evaluation employed a variety of different address databases for three different Counties in Florida. Results indicate that address point geocoding produces geocoding match rates similar to those observed for street network geocoding. Parcel geocoding generally produces much lower match rates, in particular for commercial and multi-family residential addresses. Variability in geocoding match rates between address databases and between geographic areas is substantial, reinforcing the need to strengthen the development of standards for address reference data and improved address data entry validation procedures.