Geocoding Methods Research Articles

A Novel Address-Matching Framework Based on Region Proposal

Geocoding is a fundamental component of geographic information science that plays a crucial role in various geographical studies and applications involving text data. Current mainstream geocoding methods fall into two categories: geodesic-grid prediction and address matching. However, the geodesic-grid-prediction method’s localization accuracy is hindered by the density of grid partitioning, struggling to strike a balance between prediction accuracy and grid density. Address-matching methods mainly focus on the semantics of query text. However, they tend to ignore keyword information that can be used to distinguish candidates and introduce potential interference, which reduces matching accuracy. Inspired by the human map-usage process, we propose a two-stage address-matching approach that integrates geodesic-grid prediction and text-matching models. Initially, a multi-level text-classification model is used to generate a retrieval region proposal for an input query text. Subsequently, we search for the most relevant point of interest (POI) within the region-proposal area using a semantics-based text-retrieval model. We evaluated the proposed method using POI data from the Beijing Chaoyang District. The experimental results indicate that the proposed method provides high address-matching accuracy, increasing Recall@1 by 0.55 to 1.56 percentage points and MRR@5 by 0.54 to 1.68 percentage points.

Dealing with location uncertainty for modeling network-constrained lattice data

The spatial analysis of traffic accidents has long been a useful tool for authorities to implement effective preventive measures. Initial studies were conducted at the areal level considering administrative or traffic-related units, but a more precise analysis at the street level is necessary for developing targeted interventions. In recent years, there has been a significant increase in studies conducted at the road network level, which require using new statistical techniques that are suitable for linear networks. However, modeling accident counts at the street level presents several challenges, primarily due to the need for accurate georeferenced data to correctly assign events to specific streets or road segments. Despite advancements in geocoding methods, discrepancies can still arise between the true event locations and the locations mapped by a geocoding method. In this paper, we propose a model to deal with the presence of location uncertainty and enable an analysis of accident intensity constrained to the road network. The model does not assume any specific mechanism for location uncertainty, as this reflects the most common practical scenario. By tackling this inherent problem, the proposed model aims to enhance the accuracy of accident analysis and contribute to the development of effective preventive measures for traffic safety. The model is evaluated with both a simulation study and a case study on the city of Valencia, Spain. For the latter, the proposed model reveals a greater association of road intersections with accident rates than that estimated by the standard model.

A two-step process to increase successful geocoding in publicly available police stop data

ABSTRACT Many police departments are meeting calls for transparency by releasing publicly accessible data. High-quality address locations are critical for successful and accurate geocoding, though the content and quality of that data can drastically vary across datasets. In this study, we showcase a two-step geocoding process that helps convert low-quality address locations into geo-locatable addresses using traditional geocoding and Jaro-Winkler edit distance methods with police stop data from the San Diego Police Department. For reference, only 83% of stops were geocoded when using traditional geocoding methods. By employing the Jaro-Winkler edit distance to clean the stop address strings, we were able to geocode 99% of stops. We further discuss data creation practices and solutions for data quality-related issues for police departments and researchers when using publicly available policing data.

Multi‐unit building address geocoding: An approach without indoor location reference data

AbstractAccurately mapped locations within multi‐unit properties are useful for several organizations in today's society. Published work on geocoding methods either require detailed location reference data or does not apply to multi‐unit buildings. In this research, a generalizable method is realized to map apartment addresses to their explicit locations without access to indoor location reference data based on publicly available address‐ and geospatial‐building information. The performance of this approach is measured by conducting a comparative study between a linear interpolation baseline and gradient‐boosted decision trees model. The proposed method can successfully geocode addresses across different building shapes and sizes. Furthermore, the model significantly outperforms the baseline in terms of positional accuracy proving the feasibility of approximating apartment locations by their address‐ and geospatial‐building information.

GIS to Explore the Geographical Mysteries of Oral Cancer: Geospatial Distribution of Patients Diagnosed with Oral Potentially Malignant Disorders at the University Dental Hospital Peradeniya

Background: Oral cancers which are often preceded by an oral potentially malignant disorder (OPMD) carries the highest mortality rate among all cancers in Sri Lanka. Dental Teaching Hospital, Peradeniya is one of the tertiary centres in Sri Lanka for treating OPMD with a high patient turnover. Objectives: This study aimed to determine the geospatial distribution of OPMD patients who attended Dental Hospital, Peradeniya (DHP) using geocoding methods. Materials and Methods: A retrospective study was done by obtaining data of 869 OPMD patients who visited DHP within the last 4 years to geo-code their addresses using GIS software. Results: The majority of the sample consisted of oral lichen planus (OLP) (52.47%). 29% and 18.64% of the patients were diagnosed with leukoplakia and oral submucous fibrosis (OSF) respectively. Most of the OPMD patients (60.64%) who visited the clinic were from the Kandy district and surprisingly the majority of OPMD patients had been reported from non-estate areas. The second and third highest number of patients were respectively from Nuwaraeliya (11.28%) and Kegalle (7.48%). More than half (57.19%) of the patients were living within 20 km of the hospital. Conclusion: The composition of patients with OPMDs visited the DHP had deviated from the national data. More than half of the sample residing within a reasonable travel distance to a specialist care centre.

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.

Address Geocoding Services in Geospatial-based Epidemiological Analysis: A Comparative Reliability for Domestic Disease Mapping

Geographical information system (GIS) has been used for geospatial epidemiology. Through the process, it begins with geocoding, i.e. assigning geographic coordinates to an address on a map. This process is a bridge between spatial information and its attribute data. Fortunately, some open geocoding services are available. The paper aims to examine the mapping reliability of some online geocoding services to map the spread of tuberculosis (TB) in Sarawak, Malaysia towards practical implementation in the domestic health department. The features examined the common platforms, namely QGIS, Google Map, and ArcGIS Online, were selected and explored in terms of the following variables; positional quality, speed, cost, and coverage. Based on our exploratory experiment, ArcGIS Online offers relevant mapping features for the local geocoding services of the TB locations compared to the other two platforms. But the chosen geocoding methods or services may depend on the nature of the project, cost restrictions, and the experience of an analyst. Comparison of the positional accuracy with manual reference methods (e.g GPS measurement and manual digitizing) could be further studied.

The Integration of Geospatial information and Digital archives

In this paper, through my experience of supporting the use of GLAM data in digital archives, I discussed the necessity and the methods of preserving and utilizing GLAM data by integrating geospatial information and GLAM data from the standpoint of geospatial information. In the future, GLAM data, including Japan Search, will require the development and refinement of location information metadata by GLAM institutions, the field of geographic information science, and citizen participation, such as the transfer of geocoding methods from addresses and the development of a WebGIS mechanism that allows location information metadata assignment. On the side of geographic information science, it is necessary to organize and provide bone digital geospatial information in terms of digital archiving.

Mapmonkey: The University of Adelaide Geocoding Service

IntroductionGeocoding converts street addresses to latitudes and longitudes and can be used to link spatial information to existing databases. Geocoding services universally designed for multiple countries do not always produce optimal results for Australian addresses and can be costly and difficult to use without specific knowledge/experience. Limited matching information is often provided, making it difficult to assess geocoding accuracy. Objectives and ApproachNew technology was required for address geocoding due to limitations of current geocoding methods such as a failure to incorporate the structure of Australian addresses. Software was developed for high accuracy address geocoding using hierarchical probabilistic matching to the Geocoded National Address File. The software facilitates geocoding of addresses which enables linkage of databases for broader research applications, such as geospatial analysis. ResultsMapMonkey is the University of Adelaide geocoding service for researchers. Its matching accuracy reflects the Australian address structure where state andpostcode have a higher geographic hierarchy than suburb, street name and street number, respectively, when geocoding to a house or land parcel. Conclusion / ImplicationsSmall area spatial analysis has great potential to inform our understanding of some of the more significant challenges of our time. Geocoding enables data linkage at fine spatial levels which allows more accurate socioeconomic and other data to be used in health and social research. The provision of a high accuracy geocoder will expand research capacity in data linkage and spatial analysis.

ANALYSIS OF METHODS OF UNIQUE IDENTIFICATION OF OBJECTS IN GEOSPATIAL DATA SETS

The subject of analysis is methods and means of unique identification of objects in geospatial datasets, created, registered and used in geographic information systems for various purposes and territorial coverage. Systems for unique identification of geospatial objects are classified according to basic technologies and the presence of semantic content in the composition of unique identifiers, in particular, these systems are based on the use of: 1) universal unique identifiers such as UUID; 2) direct geocoding methods using object coordinates; 3) indirect geocoding methods using geographic names and addresses. The analysis of the advantages and limitations of each of the three groups of systems for the unique identification of geospatial objects is carried out. The results of a computational experiment confirmed the possibility of reliable unique identification of buildings based on the use of an 11-digit open code location code (OLC, Google plus code). The global uniqueness of OLC-based identifiers for geospatial objects of any type requires additional research, but its uniqueness within objects of the same type is practically achievable due to the variable length of the code and the possibility of its extension with additional attributes.

Could spatial features help the matching of textual data?

Textual data is available to an increasing extent through different media (social networks, companies data, data catalogues, etc.). New information extraction methods are needed since these new resources are highly heterogeneous. In this article, we propose a text matching process based on spatial features and assessed through heterogeneous textual data. Besides being compatible with heterogeneous data, it comprises two contributions: first, spatial information is extracted for comparison purposes and subsequently stored in a dedicated spatial textual representation (STR); and then two transformations are applied on STR to improve the spatial similarity estimation. This article outlines the proposed approach with new contributions: (i) a new geocoding methods using general co-occurrences between entities, and (ii) a thorough evaluation followed by (iii) an in-depth discussion. The results obtained on two corpora demonstrate that good spatial matches (≈ 80% precision on major criteria) can be obtained between the most similar STRs with further enhancement achieved via STR transformation.

Geocoding Error, Spatial Uncertainty, and Implications for Exposure Assessment and Environmental Epidemiology.

Although environmental epidemiology studies often rely on geocoding procedures in the process of assigning spatial exposure estimates, geocoding methods are not commonly reported, nor are consequent errors in exposure assignment explored. Geocoding methods differ in accuracy, however, and, given the increasing refinement of available exposure models for air pollution and other exposures, geocoding error may account for an increasingly larger proportion of exposure misclassification. We used residential addresses from a reasonably large, dense dataset of asthma emergency department visits from all New York City hospitals (n = 21,183; 26.9 addresses/km2), and geocoded each using three methods (Address Point, Street Segment, Parcel Centroid). We compared missingness and spatial patterning therein, quantified distance and directional errors, and quantified impacts on pollution exposure estimates and assignment to Census areas for sociodemographic characterization. Parcel Centroids had the highest overall missingness rate (38.1%, Address Point = 9.6%, Street Segment = 6.1%), and spatial clustering in missingness was significant for all methods, though its spatial patterns differed. Street Segment geocodes had the largest mean distance error (µ = 29.2 (SD = 26.2) m; vs. µ = 15.9 (SD = 17.7) m for Parcel Centroids), and the strongest spatial patterns therein. We found substantial over- and under-estimation of pollution exposures, with greater error for higher pollutant concentrations, but minimal impact on Census area assignment. Finally, we developed surfaces of spatial patterns in errors in order to identify locations in the study area where exposures may be over-/under-estimated. Our observations provide insights towards refining geocoding methods for epidemiology, and suggest methods for quantifying and interpreting geocoding error with respect to exposure misclassification, towards understanding potential impacts on health effect estimates.

Testing the Spatial Accuracy of Address-Based Geocoding for Gunshot Locations

We assess the positional accuracy of address-based geocoding of shooting incidents relative to the location recorded via acoustic gunshot detection technology. This provides a test of the accuracy of typical address-based geocoding methods used in crime analysis, as well as evidence for how much accuracy one gains when using sensors. Examining more than 1,000 shooting incidents in Wilmington, North Carolina, we find that the majority of address-based incidents are quite accurate, on average within 60 feet of the actual location (using a street centerline geocoder) or within 90 feet (using Google rooftop geocoding). Based on the incident narrative, however, we identify a subset of transcription errors in over 10 percent of the cases that increases the distance between the true shooting location and that geocoded using address data. This suggests that mechanisms to prevent human errors might be more frugal than those relying on sensors in geocoding shooting incidents.

Information Technology and the Experience of Creating a Database on the History of the 20th Century in the Personalities

This article is devoted to the description of the experience of creating a database in which more than 36 thousand people are collected. Originally developed for informational purposes, this database and the Internet resource created on its basis have been recognized not only in Russia, but also abroad. On the basis of the collected data on historical personalities, numerous scientific historical studies were conducted. These studies were made possible thanks to a well-designed data structure and clarity of the presentation of the material. Gained invaluable experience working with personal information sources. Implemented mechanisms for displaying geographic data of personalities using the Open street map, Google map and Yandex map services. Services have been developed for creating multimedia collections based on personal data, a service for preparing publications of personal reference books. The article describes the prospects for the development of historical database tools to simplify the conduct of historical research, namely: identifying hidden links between database objects, creating multidimensional data representations, and applying retrospective geocoding methods.

Updating the global occurrence of Culicoides imicola, a vector for emerging viral diseases

Culicoides imicola is the main vector transmitting viruses causing animal diseases such as Bluetongue, African Horse Sickness, and Schmallenberg. It has become widely distributed, with reports from South Africa to southern Europe, and from western Africa to southern China. This study presents a global compendium of Culicoides imicola occurrence between 1943 and 2018, reflecting the most recently compiled and harmonized global dataset derived from peer-reviewed literature. The procedures used in producing the data, as well as the geo-coding methods, database management and technical validation procedures are described. The study provides an updated and comprehensive global database of C. imicola occurrence, consisting of 1 039 geo-coded records from 50 countries. The datasets can be used for risk mapping of the diseases transmitted by C. imicola as well as to develop the global habitat suitability for the vector.

A spatial database of health facilities managed by the public health sector in sub Saharan Africa

Health facilities form a central component of health systems, providing curative and preventative services and structured to allow referral through a pyramid of increasingly complex service provision. Access to health care is a complex and multidimensional concept, however, in its most narrow sense, it refers to geographic availability. Linking health facilities to populations has been a traditional per capita index of heath care coverage, however, with locations of health facilities and higher resolution population data, Geographic Information Systems allow for a more refined metric of health access, define geographic inequalities in service provision and inform planning. Maximizing the value of spatial heath access requires a complete census of providers and their locations. To-date there has not been a single, geo-referenced and comprehensive public health facility database for sub-Saharan Africa. We have assembled national master health facility lists from a variety of government and non-government sources from 50 countries and islands in sub Saharan Africa and used multiple geocoding methods to provide a comprehensive spatial inventory of 98,745 public health facilities.

A deep learning approach for rooftop geocoding

AbstractGeocoding has become a routine task for many research investigations to conduct spatial analysis. However, the output quality of geocoding systems is found to impact the conclusions of subsequent studies that employ this workflow. The published development of geocoding systems has been limited to the same set of interpolation methods and reference data sets for quite some time. We introduce a novel geocoding approach utilizing object detection on remotely sensed imagery based on a deep learning framework to generate rooftop geocoding output. This allows geocoding systems to use and output exact building locations without employing typical geocoding interpolation methods or being completely limited by the availability of reference data sets. The utility of the proposed approach is demonstrated over a sample of 22,481 addresses resulting in significant spatial error reduction and match rates comparable to typical geocoding methods. For different land‐use types, our approach performs better on low‐density residential and commercial addresses than on high‐density residential addresses. With appropriate model setup and training, the proposed approach can be extended to search different object locations and to generate new address and point‐of‐interest reference data sets.

Positional Accuracy of Online Geocoding Services: Case Study of Bhaktapur District

Data is food for Information Systems and location data is basis for GIS services. Geocoding services provide this data by converting Street Addresses like NCIT, Balkumari to corresponding geographic coordinates. These coordinates are then used in data processing to deliver services. Many services (especially location based), FourSquare, Uber, depend on these services for operation. Nepalese market is also increasingly using these services like Tootle, sarathi cab. Till now nobody knows how accurate the result of such services are. There exist multiple places with same name. Some names are not actual but referential. Accuracy of the services depend on the underlying database, the method used, the actual geographic location and also the actual query. Different methods yield different results. The assessment of accuracy and suitability of the geocoding services has not been conducted, yet they are being used extensively. The objective of this study is to compare the positional difference between two Geocoding methods, OpenStreetMap (OSM) Nominatim Service and Google Geocoding Services and compare them with standard government datasets to measure their discrepancy. For reference, settlement data from NGIID was used. Addresses were first geocoded to street level and positional difference in the results were calculated using havensire formula. The discrepancies were categorized into intervals of 100m. Out of 267 location points, 118 result were found in Nominatim, whereas only 86 were found in Google. Average discrepancy for Nominatim result was 175m and for Google it was 1810m. Comparisons show minimal difference in median and minimum values, while there were larger differences in the maximum value. Nominatim delivered comparatively accurate results and found more addresses than google. Google on the other hand gave huge mismatches for some cases. The study found out that the databases are missing in both cases as shown by the no of “not found" cases and that the results from Nominatim are more reliable than that of Google because of its hierarchical matching system and user friendly interface.

Accuracy of two geocoding methods for geographic information system-based exposure assessment in epidemiological studies

BackgroundEnvironmental exposure assessment based on Geographic Information Systems (GIS) and study participants’ residential proximity to environmental exposure sources relies on the positional accuracy of subjects’ residences to avoid misclassification bias. Our study compared the positional accuracy of two automatic geocoding methods to a manual reference method.MethodsWe geocoded 4,247 address records representing the residential history (1990–2008) of 1,685 women from the French national E3N cohort living in the Rhône-Alpes region. We compared two automatic geocoding methods, a free-online geocoding service (method A) and an in-house geocoder (method B), to a reference layer created by manually relocating addresses from method A (method R). For each automatic geocoding method, positional accuracy levels were compared according to the urban/rural status of addresses and time-periods (1990–2000, 2001–2008), using Chi Square tests. Kappa statistics were performed to assess agreement of positional accuracy of both methods A and B with the reference method, overall, by time-periods and by urban/rural status of addresses.ResultsRespectively 81.4% and 84.4% of addresses were geocoded to the exact address (65.1% and 61.4%) or to the street segment (16.3% and 23.0%) with methods A and B. In the reference layer, geocoding accuracy was higher in urban areas compared to rural areas (74.4% vs. 10.5% addresses geocoded to the address or interpolated address level, p < 0.0001); no difference was observed according to the period of residence. Compared to the reference method, median positional errors were 0.0 m (IQR = 0.0-37.2 m) and 26.5 m (8.0-134.8 m), with positional errors <100 m for 82.5% and 71.3% of addresses, for method A and method B respectively. Positional agreement of method A and method B with method R was ‘substantial’ for both methods, with kappa coefficients of 0.60 and 0.61 for methods A and B, respectively.ConclusionOur study demonstrates the feasibility of geocoding residential addresses in epidemiological studies not initially recorded for environmental exposure assessment, for both recent addresses and residence locations more than 20 years ago. Accuracy of the two automatic geocoding methods was comparable. The in-house method (B) allowed a better control of the geocoding process and was less time consuming.

Smartphone-assisted spatial data collection improves geographic information quality: pilot study using a birth records dataset.

It is well known that the conventional, automated geocoding method based on self-reported residential addresses has many issues. We developed a smartphone-assisted aerial image-based method, which uses the Google Maps application programming interface as a spatial data collection tool during the birth registration process. In this pilot study, we have tested whether the smartphone-assisted method provides more accurate geographic information than the automated geocoding method in the scenario when both methods can get the address geocodes. We randomly selected 100 well-geocoded addresses among women who gave birth in Alachua county, Florida in 2012. We compared geocodes generated from three geocoding methods: i) the smartphone-assisted aerial image-based method; ii) the conventional, automated geocoding method; and iii) the global positioning system (GPS). We used the GPS data as the reference method. The automated geocoding method yielded positional errors larger than 100 m among 29.3% of addresses, while all addresses geocoded by the smartphone-assisted method had errors less than 100 m. The positional errors of the automated geocoding method were greater for apartment/condominiums compared with other dwellings and also for rural addresses compared with urban ones. We conclude that the smartphone-assisted method is a promising method for perspective spatial data collection by improving positional accuracy.