Geocoding Tools Research Articles

Challenges in Geocoding: An Analysis of R Packages and Web Scraping Approaches

Georeferenced data are crucial for addressing societal spatial challenges, as most corporate and governmental information is location-compatible. However, many open-source solutions lack automation in geocoding while ensuring quality. This study evaluates the functionalities of various R packages and their integration with external APIs for converting postal addresses into geographic coordinates. Among the fifteen R methods/packages reviewed, tidygeocoder stands out for its versatility, though discrepancies in processing times and missing values vary by provider. The accuracy was assessed by proximity to original dataset coordinates (Madrid street map) using a sample of 15,000 addresses. The results indicate significant variability in performance: MapQuest was the fastest, ArcGIS the most accurate, and Nominatim had the highest number of missing values. To address these issues, an alternative web scraping methodology is proposed, substantially reducing the error rates and missing values, but raising potential legal concerns. This comparative analysis highlights the strengths and limitations of different geocoding tools, facilitating better integration of geographic information into datasets for researchers and social agents.

Обработка и анализ сообщений для геокодирования аномальных ситуаций

The purpose of the research was to study and implement methods for collecting and processing data obtained from social networks to extract useful information about emergency situations. Information extracted from social media posts can help detect the fact of an emergency and establish its approximate geographical location based on the text of the record reporting the incident. Tools were designed and developed that implement the collection and analysis of records from the VK social network communities. A tool has been created to extract the geographical location described in the analyzed text. Basic natural language processing methods, named entity recognition methods, frequency analysis and geocoding tools are used to extract information. The developed programs may have practical significance for public authorities, security services and other interested organizations in the field of ensuring the safety and protection of the population.

Quality of automatic geocoding tools: a study using addresses from hospital record files in Temuco, Chile.

Automatic geocoding methods have become popular in recent years, facilitating the study of the association between health outcomes and the place of living. However, rather few studies have evaluated geocoding quality, with most of them being performed in the US and Europe. This article aims to compare the quality of three automatic online geocoding tools against a reference method. A subsample of 300 handwritten addresses from hospital records was geocoded using Bing, Google Earth, and Google Maps. Match rates were higher (> 80%) for Google Maps and Google Earth compared with Bing. However, the accuracy of the addresses was better for Bing with a larger proportion (> 70%) of addresses with positional errors below 20m. Generally, performance did not vary for each method for different socioeconomic status. Overall, the methods showed an acceptable, but heterogeneous performance, which may be a warning against the use of automatic methods without assessing quality in other municipalities, particularly in Chile and Latin America.

Технология извлечения геоатрибутированных сущностей для визуального представления пространственной связности объектов на основе автоматизированной генерации картосхем

This paper considers the problem of extracting geoattributed entities from natural language texts to visualize the spatial relations of geographical objects. For visualization we use the technology of automated generation of schematic maps as subject-oriented components of geographic information systems. The paper describes the information technology that allows extracting geoattributed entities from natural language texts by combining several approaches. These are the neural network approach, the rule-based approach and the approach based on the use of lexico-syntactic patterns for the analysis of natural language texts. For data visualization we propose to use automated geocoding tools in conjunction with the capabilities of modern geographic information systems. The result of this technology is a cartogram that displays the spatial relations of the objects mentioned in the text.

Automated geoimage generation of Arctic industrial and natural complexes for regional management information support systems

The problem of monitoring and management of industrial and natural complexes in the Arctic is an important problem, without solving which the effective development of the Arctic zones of the Russian Federation is impossible. Specialized information systems are actively used to support decision-making on planning and managing various types of activities in the Arctic, as well as the socio-economic development of the Arctic territories of the Russian Federation. Spatial information visualization tools are an important part of such systems. Visual representation of data is one of the most convenient types of information representation for human processing. This paper describes a method of automated geoimage generation for decision support systems based on the analysis of natural language texts. The use of such geoimages reduces the complexity of decision making for the management tasks of spatially distributed bio-socio-economic systems of the region. The paper describes the formulation of the research problem, review of geocoding tools and services, the original technology of automated geoimages (digital maps) generation based on the use of cloud geoservices. Particular attention is paid to the problem of recognition of named entities in natural language text. The recognition results are used as input data for geocoding and geovisualization process. As a result of the method, the text is converted into a geoimage (map) which can be used to support decision-making by a decision maker. In the final part of the paper, plans for further work are described to develop the capabilities of the automated geoimage generation method for decision support systems based on the analysis of natural language texts.

QualiOSM: An Architecture to Improve Data Completeness on OpenStreetMap

OpenStreetMap (OSM) is a large spatial database in which geographic information is voluntarily contributed by thousands of users. In Geographic Information Systems (GIS), and more specifically, in Volunteered Geographic Information (VGI), as in the case of OSM, the issue of data completeness is a constant concern, since users without technical knowledge actively participate in the processes of including, editing and excluding data. Also in thecase of OSM, users can add information to the objects assigning special labels for them. These labels are popularly called tags, and the process of assigning them to objects contributes to improving the attribute completeness, an important metric of data quality. In this context, this article proposes the QualiOSM architecture, which generates an automatic tag adder with the purpose of improving the completeness of address information for OSM objects in Brazil, using the reverse geocoding tools Nominatim, CEP Aberto and the database from Correios. The QualiOSM architecture showed good results for improving the completeness of city, neighborhood and street information in OSM objects, especially in scenarios of large urban centers, where the level of mapping is usually better compared to scenarios in rural or peripheral environments.

Geocoding cryptosporidiosis cases in Ireland (2008\u20132017)\u2014development of a reliable, reproducible, multiphase geocoding methodology

BackgroundGeocoding (the process of converting a text address into spatial data) quality may affect geospatial epidemiological study findings. No national standards for best geocoding practice exist in Ireland. Irish postcodes (Eircodes) are not routinely recorded for infectious disease notifications and > 35% of dwellings have non-unique addresses. This may result in incomplete geocoding and introduce systematic errors into studies.AimsThis study aimed to develop a reliable and reproducible methodology to geocode cryptosporidiosis notifications to fine-resolution spatial units (Census 2016 Small Areas), to enhance data validity and completeness, thus improving geospatial epidemiological studies.MethodsA protocol was devised to utilise geocoding tools developed by the Health Service Executive’s Health Intelligence Unit. Geocoding employed finite-string automated and manual matching, undertaken sequentially in three additive phases. The protocol was applied to a cryptosporidiosis notification dataset (2008–2017) from Ireland’s Computerised Infectious Disease Reporting System. Outputs were validated against devised criteria.ResultsOverall, 92.1% (4266/4633) of cases were successfully geocoded to one Small Area, and 95.5% (n = 4425) to larger spatial units. The proportion of records geocoded increased by 14% using the multiphase approach, with 5% of records re-assigned to a different spatial unit.ConclusionsThe developed multiphase protocol improved the completeness and validity of geocoding, thus increasing the power of subsequent studies. The authors recommend capturing Eircodes ideally using application programming interface for infectious disease or other health-related datasets, for more efficient and reliable geocoding. Where Eircodes are not recorded/available, for best geocoding practice, we recommend this (or a similar) quality driven protocol.

Assessment of automatic geocoding tools in Temuco, Chile

TPS 651: Air pollution exposure modeling 1, Exhibition Hall, Ground floor, August 27, 2019, 3:00 PM - 4:30 PM Automatic geocoding process is carried out in many epidemiological studies. However, the quality of the process has been scarcely evaluated. The objective of this work is to compare the performance of three automatic online geocoding tools using addresses from hand-written hospital files. To do this, from an initial set of transcribed addresses a random sample was geocoded using a gold-standard approach, then the same addresses were geocoded using the three automatic approaches. Analyses were based on a retrospective pregnancy cohort study including 15,500 pregnant women who lived in two cities: Temuco and Padre Las Casas. A sampling design stratified by city was carried out. A sub-sample with 300 random addresses were selected: 200 from Temuco and 100 from Padre Las Casas. The geocoding process of the sub-sample was manually conducted. Every address was located in front of the house using Google Street View. If the address was not found, then a Global Positional System (GPS) receiver was used to obtain coordinates. Then the same addresses were geocoded using the three automatic approaches: Google using Qgis, Google Earth using R and Bing using R. Measures of assessment of performance included completeness, match agreement and positional error. Analyses showed 90% of manual gecoding success. Automated geocoding completeness were 90% for Google, 57% for Bing and 84% for Google Earth. It was observed differences between cities. Some neighborhoods have completeness lower than 75%. Bing was not able to find addresses in Padre Las Casas. Positional error was estimated 11m (SD: 13.9) for Bing; 80m (SD: 446.3) for Google Earth, and 83m (SD: 364.5) for Google. There are differences between cities: Temuco has the lower positional error. Analyses showed that quality of geocoding differs by city and neighborhood. Could be advisable to study locally geocoding performance before to apply it to epidemiological studies.

Evaluating two freely available geocoding tools for geographical inconsistencies and geocoding errors

BackgroundGeocoding is highly prone to error for various reasons. This paper examines the geographical inconsistencies associated with geocoding errors seen when using two freely available geocoding tools, Google Sheets and ggmap.MethodsTwo hundred restaurants, all recipients of California’s Center of Excellence award, were selected for the analysis. The geocoded addresses were plotted on maps using QGIS, Google Maps, OpenStreetMap (OSM), and Google Earth for visualization, comparison, and validation. A stepwise method of analyzing the geographical inconsistencies is provided that can be adapted for any locational analytics.Results and discussionBoth Google Sheets and ggmap were able to successfully geocode all 200 addresses, but ggmap incorrectly geocoded eight addresses as being more than 2,000 miles from their actual location. Addresses containing the ampersand character, &, caused ggmap to incorrectly geocode their location. After replacing the ampersand with the word and, ggmap was able to correctly geocode those addresses. The corrected locations plotted on Google Maps and OSM were similar, and they exactly matched the actual locations when plotted on Google Earth.ConclusionsBoth Google Sheets and ggmap are equally capable of geocoding physical locations, but R users are advised that addresses for geocoding must be free of the ampersand character if correct results are to be obtained. In addition, geocoded outputs should be plotted on a map using QGIS, ArcGIS, Google Maps, OSM, R, or any other such mapping tools for visualization and validation. This will ensure a high-quality geospatial analysis of places or events when locational information is vital for decision-making.

Georeferencing Brazilian Highway Addresses

The usage of georeferencing as a search tool has suffered a massification after the diffusion of technologies developed by Google and Microsoft. In this context, problems related to the location of properties situated on highways are becoming quite usual in Brazil, because properties registration in highway is permitted in the country and this invalidates the dynamics of the georeferencing process. Therefore, a growing number of geotechnology users which desire to localize properties on highways, find inaccurate or amiss geographical information. This research focus is to present an enumeration method for highway properties and, consequently, the possibility of a precise georeferencing. The presented method contributes to turning possible the precision of this georeferencing process and it will also be relevant for geotechnology diffusion considering its integration to geographic information systems which include geocoding tools.

Desigualdades educacionais em contextos urbanos: um estudo da geografia de oportunidades educacionais na cidade do Rio de Janeiro

O objetivo do trabalho é trazer uma contribuição teórica dos estudos da sociologia urbana, bem como a contribuição metodológica das análises espaciais (georreferenciamento), para analisar as desigualdades educacionais em grandes centros urbanos. Ao integrar a dimensão das desigualdades educacionais às dinâmicas de segregação residencial desses centros, traz questões até então subdimensionadas nos estudos sobre desigualdades educacionais. Como estratégia analítica, selecionamos a cidade do Rio de Janeiro para exemplificar as potencialidades da abordagem teórico-metodológica adotada. As análises reuniram informações de diferentes bases de dados, tais como Prova Brasil (2011) e Censo Escolar (2011). Por fim, o artigo discute experiências recentes de políticas educacionais que utilizam instrumentos de geoprocessamento e/ou que focalizam escolas em territórios vulneráveis e suas consequências para distribuição de oportunidades educacionais.

A multi-stage approach to maximizing geocoding success in a large population-based cohort study through automated and interactive processes

To enable spatial analyses within a large, prospective cohort study of nearly 86,000 adults enrolled in a 12-state area in the southeastern United States of America from 2002-2009, a multi-stage geocoding protocol was developed to efficiently maximize the proportion of participants assigned an address level geographic coordinate. Addresses were parsed, cleaned and standardized before applying a combination of automated and interactive geocoding tools. Our full protocol increased the non-Post Office (PO) Box match rate from 74.5% to 97.6%. Overall, we geocoded 99.96% of participant addresses, with only 5.2% at the ZIP code centroid level (2.8% PO Box and 2.3% non-PO Box addresses). One key to reducing the need for interactive geocoding was the use of multiple base maps. Still, addresses in areas with population density <44 persons/km2 were much more likely to require resource-intensive interactive geocoding than those in areas with >920 persons/km2 (odds ratio (OR) = 5.24; 95% confidence interval (CI) = 4.23, 6.49), as were addresses collected from participants during in-person interviews compared with mailed questionnaires (OR = 1.83; 95% CI = 1.59, 2.11). This study demonstrates that population density and address ascertainment method can influence automated geocoding results and that high success in address level geocoding is achievable for large-scale studies covering wide geographical areas.

Comparative evaluation and analysis of online geocoding services

Geocoding is an uncertain process that associates an address or a place name with geographic coordinates. Traditionally, geocoding is performed locally on a stand-alone computer with the geocoding tools usually bundled in GIS software packages. The use of such tools requires skillful operators who know about the issues of geocoding, that is, reference databases and complicated geocoding interpolation techniques. These days, with the advancement in the Internet and Web services technologies, online geocoding provides its functionality to the Internet users with ease; thus, they are often unaware of such issues. With an increasing number of online geocoding services, which differ in their reference databases, the geocoding algorithms, and the strategy for dealing with inputs and outputs, it is crucial for the service requestors to realize the quality of the geocoded results of each service before choosing one for their applications. This is primarily because any errors associated with the geocoded addresses will be propagated to subsequent decisions, activities, modeling, and analysis. This article examines the quality of five online geocoding services: Geocoder.us, Google, MapPoint, MapQuest, and Yahoo!. The quality of each geocoding service is evaluated with three metrics: match rate, positional accuracy, and similarity. A set of addresses from the US Environmental Protection Agency (EPA) database were used as a baseline. The results were statistically analyzed with respect to different location characteristics. The outcome of this study reveals the differences among the online geocoding services on the quality of their geocoding results and it can be used as a general guideline for selecting a suitable service that matches an application's needs.

A comparison of address point, parcel and street geocoding techniques

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.

Influence of geocoding quality on environmental exposure assessment of children living near high traffic roads

BackgroundThe 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 epidemiological studies. This study determined the effect of the positional error in street geocoding on the analysis of traffic-related air pollution on children.MethodsFor a case-study of a large sample of school children in Orange County, Florida (n = 104,865) the positional error of street geocoding was determined through comparison with a parcel database. The effect of this error was evaluated by analyzing the proximity of street and parcel geocoded locations to road segments with high traffic volume and determining the accuracy of the classification using the results of street geocoding. Of the original sample of 163,886 addresses 36% were not used in the final analysis because they could not be reliably geocoded using either street or parcel geocoding. The estimates of positional error can therefore be considered conservative underestimates.ResultsStreet geocoding was found to have a median error of 41 meters, a 90th percentile of 100 meters, a 95th percentile of 137 meters and a 99th percentile of 273 meters. These positional errors were found to be non-random in nature and introduced substantial bias and error in the estimates of potential exposure to traffic-related air pollution. Street geocoding was found to consistently over-estimate the number of potentially exposed children at small distances up to 250 meters. False positives and negatives were also found to be very common at these small distances.ConclusionResults of the case-study presented here strongly suggest that typical street geocoding is insufficient for fine-scale analysis and more accurate alternatives need to be considered.

Improving geocoding practices: evaluation of geocoding tools.

This study examined the sources of error involved in geocoding, by systematically evaluating the strengths and weaknesses of three widely used tools for geocoding. We tested them against a random sample of addresses from a state administrative address master file and found considerable variation in identification of census block geocodes of addresses. This high variation was mainly attributable to differences in preprocessing of addresses before geocoding and the reference street data used for geocoding. Preprocessing includes not only parsing and standardizing, but also correcting addresses against the US Postal Service Zip+4 Database, the master mailing address database maintained and updated regularly by USPS.