Investigating the quality of reverse geocoding services using text similarity techniques and logistic regression analysis

Kılıç, Batuhan; Gülgen, Fatih

doi:10.1080/15230406.2020.1746198

Cited by 6 publications

(10 citation statements)

References 44 publications

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“…This confusion arises mainly because the study area is a historical and touristic settlement characterized by high density, meaning numerous POIs per unit area exist. Although the postal addresses of two adjacent buildings with different door numbers are different, their entrances can be close to each other (Kilic & Gülgen, 2020a). On the contrary, current postal address retrieval solutions often only focus on distance, ignoring spatial topology linkages.…”

Section: Evaluation Results For Address Accuracymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Reverse geocoding can be performed in either a conventional (classical) or online manner. Online reverse geocoding operates on a network-accessible system and is usually accessible through the internet using a web application programming interface (API), while conventional reverse geocoding uses the core packages included in GIS software (Kilic & Gülgen, 2020a). Online reverse geocoding is mainly provided by commercial map providers such as Google Maps, Bing Maps, Esri, and HERE Maps to the public and end-users.…”

Section: Introductionmentioning

confidence: 99%

“…In all domains of Geography and Geographic Information Science (GISc), georeferenced location plays a crucial role. With advancements in Earth observation technologies, it has become easier to establish a connection between general data and their geospatial points on digital maps (Kilic & Gülgen, 2020a; Ningsheng et al, 2015). The process of transforming between the spatial and place‐based representations of a location, both forward and backward, is an important task that forms the basis of various research, applications, and system architectures (McKenzie & Janowicz, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Effects of reverse geocoding on OpenStreetMap tag quality assessment

Kılıç¹,

Hacar

Gülgen³

2023

Transactions in GIS

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OpenStreetMap (OSM), a widely‐used open‐source geographic information system platform, provides a vast geographic dataset in which users contribute both geometric information (nodes, ways, and relations) and semantic information (tags). This method of voluntary contributions is governed by the collective effort of the users. It is widely acknowledged that the quantity of tag information is substantial, but its quality is often poor. Researchers are therefore trying to assess the quality of the tags and enhance the data through various integration experiments. This article investigates the validity of the tags for geographical objects in metropolitan areas using municipal data and a reverse geocoding technique. The proposed method evaluates the data quality and the matching process carried out by reverse geocoding, using municipal points of interest as a reference. The accuracy of the tag and address information and road network centrality metrics were assessed for the OSM objects that were matched to the locations of interest. The tags were found to match the points of interest with an accuracy of 88%. Furthermore, the tag values were categorized and analyzed based on their similarity. It is concluded that in metropolitan settings where centers of interest are closely located, the accuracy of tags and addresses tends to decrease.

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Section: Evaluation Results For Address Accuracymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Effects of reverse geocoding on OpenStreetMap tag quality assessment

Kılıç¹,

Hacar

Gülgen³

2023

Transactions in GIS

Self Cite

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show abstract

“…The Jaro-Winkler process proposed by William E. Winkler is refined by the Jaro Distance algorithm [14]. Jaro's distance algorithm determines the similarity value of two words by counting the number of characters that correspond to the two words that are not too far away and reducing the number of characters that match up to half of the number of characters undergoing transposition.…”

Section: Jaro-winkler Distancementioning

confidence: 99%

Performance Comparison of Similarity Measure Algorithm as Data Preprocessing Stage: Text Normalization in Bahasa

Finansyah

Afiahayati

Sutanto

2022

SJI

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Purpose: More and more data are stored in text form due to technological developments, making text data processing more difficult. It also causes problems in the text preprocessing algorithm, one of which is when two texts are identical, but are considered distinct by the algorithm. Therefore, it is necessary to normalize the text to get the standard form of words in a particular language. Spelling correction is often used to normalize text, but for Bahasa Indonesia, there has not been much research on the spell correction algorithm. Thus, there needs to be a comparison of the most appropriate spelling correction algorithms for the normalization process to be effective.Methods: In this study, we compared three algorithms, namely Levenshtein Distance, Jaro-Winkler Distance, and Smith-Waterman. These algorithms were evaluated using questionnaire data and tweet data, which both are in Bahasa Indonesia.Result: The fastest normalization time is obtained by the Jaro-Winkler, taking an average of 31.01 seconds for questionnaire data and 59.27 seconds for tweet data. The best accuracy is obtained by the Levenshtein Distance with a value of 44.90% for the questionnaire data and 60.04% for the tweet data. Novelty: The novelty of this research is to compare the similarity measure algorithm in Bahasa Indonesia. Therefore, the most suitable similarity measure algorithm for Bahasa Indonesia will be obtained.

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Unveiling the impact of machine learning algorithms on the quality of online geocoding services: a case study using COVID-19 data

Kilic,

Bayrak,

Gülgen

et al. 2024

J Geogr Syst

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In today's era, the address plays a crucial role as one of the key components that enable mobility in daily life. Address data are used by global map platforms and location-based services to pinpoint a geographically referenced location. Geocoding provided by online platforms is useful in the spatial tracking of reported cases and controls in the spatial analysis of infectious illnesses such as COVID-19. The first and most critical phase in the geocoding process is address matching. However, due to typographical errors, variations in abbreviations used, and incomplete or malformed addresses, the matching can seldom be performed with 100% accuracy. The purpose of this research is to examine the capabilities of machine learning classifiers that can be used to measure the consistency of address matching results produced by online geocoding services and to identify the best performing classifier. The performance of the seven machine learning classifiers was compared using several text similarity measures, which assess the match scores between the input address data and the services' output. The data utilized in the testing came from four distinct online geocoding services applied to 925 addresses in Türkiye. The findings from this study revealed that the Random Forest machine learning classifier was the most accurate in the address matching procedure. While the results of this study hold true for similar datasets in Türkiye, additional research is required to determine whether they apply to data in other countries.

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Investigating the quality of reverse geocoding services using text similarity techniques and logistic regression analysis

Cited by 6 publications

References 44 publications

Effects of reverse geocoding on OpenStreetMap tag quality assessment

Effects of reverse geocoding on OpenStreetMap tag quality assessment

Performance Comparison of Similarity Measure Algorithm as Data Preprocessing Stage: Text Normalization in Bahasa

Unveiling the impact of machine learning algorithms on the quality of online geocoding services: a case study using COVID-19 data

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