Geocoding Method Research Articles

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.

‘Unmasking’ masked address data: A medoid geocoding solution

In recent years, there has been a consistent push for more open data initiatives, particularly for datasets collected by public agencies or groups that receive public funding. However, there is a tension between the release of open data and the preservation of individual and household privacy, whose balance shifts due to increased data availability, the sophistication of analysis techniques, and the computational power available to users. As a result, data masking is a standard tool used to preserve privacy. This is a process in which the data publishers obfuscate some identifying features in the dataset while attempting to maintain as much accuracy and precision as possible. For spatial datasets, the geocoding of administratively-masked data has been a consistent problem. Here, we present a medoid-based technique that geocodes masked data while minimizing the spatial uncertainty associated with the masking approach. Unfortunately, many commercial geocoding software packages either fail to geocode administratively-masked data or provide false positives by assigning points to city or street centroids. We demonstrate the results of our medoid-based geocoding approach by comparing it to commercial geocoding software. The results suggest that a medoid geocoding approach is mechanically simple to deploy and maximizes the spatial accuracy of the resulting geocodes.•Administratively-masked data are difficult to geocode•A medoid geocoding method maximizes geocoding accuracy•This method outperforms commercial geocoding software

Association between residential greenspace and health-related quality of life in children aged 0–12 years

IntroductionGreenspaces generate several perceived health benefits, including an overall improvement in the quality of life. However, little is known about the effects of greenspaces through pregnancy and early childhood in promoting health-related quality of life (HRQoL) among children. MethodParticipants were from the Mothers and their Children's Health Study (MatCH), a 2016/17 sub-study of a national prospective study since 1996 known as the Australian Longitudinal Study on Women's Health (ALSWH). Mothers (n=3,048) self-reported on their three youngest children aged under 13 years (n=5,799, mean=7.0 years, s.d=3.2 years) using the Pediatric Quality of Life Inventory (PedsQL) to measure their HRQoL. Since 1996, annual exposure to green and non-green vegetation was measured using two remote sensing indicators: Normalized Difference Vegetation Index (NDVI) and fractional cover of non-photosynthetic vegetation (fNPV), respectively, for 100 m and 500 m buffer zone around maternal residential address. Multiple exposure windows were calculated including during pregnancy, the first year of life and child's lifetime exposure. Generalised estimating equations (GEE) models, adjusting for potential confounders, were used for analyses. ResultsA 1 standard deviation increase in NDVI greenness within 500 m buffer around the home at early life and during childhood was positively associated with higher HRQoL in the total scores and psychological health summary scores in the crude model only. No association was found between fNPV (non-green vegetation) at 100 m and 500 m circular buffers and children's HRQoL. The overall findings from our models remained consistent based on a series of sensitivity analyses, including the impact of maternal residential mobility status and geocoding method on the effect estimates. ConclusionOur study revealed that surrounding residential greenspace was not associated with children's HRQoL. Further longitudinal studies are required to better understand the influence of greenspace at different periods of exposure on the health and wellbeing of children.

Efficient and Reliable Geocoding of German Twitter Data to Enable Spatial Data Linkage to Official Statistics and Other Data Sources

More and more, social scientists are using (big) digital behavioral data for their research. In this context, the social network and microblogging platform Twitter is one of the most widely used data sources. In particular, geospatial analyses of Twitter data are proving to be fruitful for examining regional differences in user behavior and attitudes. However, ready-to-use spatial information in the form of GPS coordinates is only available for a tiny fraction of Twitter data, limiting research potential and making it difficult to link with data from other sources (e.g., official statistics and survey data) for regional analyses. We address this problem by using the free text locations provided by Twitter users in their profiles to determine the corresponding real-world locations. Since users can enter any text as a profile location, automated identification of geographic locations based on this information is highly complicated. With our method, we are able to assign over a quarter of the more than 866 million German tweets collected to real locations in Germany. This represents a vast improvement over the 0.18% of tweets in our corpus to which Twitter assigns geographic coordinates. Based on the geocoding results, we are not only able to determine a corresponding place for users with valid profile locations, but also the administrative level to which the place belongs. Enriching Twitter data with this information ensures that they can be directly linked to external data sources at different levels of aggregation. We show possible use cases for the fine-grained spatial data generated by our method and how it can be used to answer previously inaccessible research questions in the social sciences. We also provide a companion R package, nutscoder, to facilitate reuse of the geocoding method in this paper.

Transmission elimination of lymphatic filariasis using spatial autocorrelation

At present, spatial analysis has been used on epidemiology. Spatial analysis was used to determine the environmental risk that influence to transmission of Filariasis. The aim of the study was to identify, at industrial area, the environment determinant that are associated with Filariasis cases in Pekalongan City. The geocoding method was applied on the prevalence of cases to determine the pattern of spatial. Spatial autocorrelation was used to determine the effect of the environment on filariasis transmission. The kernel method was used to determine the density of filariasis cases. Based on the spatial analysis, the statistical values associated with the correlation between the risk of filariasis transmission and environmental factors were obtained. The correlation value of the influence of the environment on the transmission of Filariasis was statistically significant, this coefficient is 0.312. The value of R indicates that the spatial pattern of filariasis cases forms a cluster pattern. The Moran’s index calculation obtained a positive spatial autocorrelation value of 0.44 with z-score is 16.05 and P-value is 0.00. Spatial autocorrelation was useful to determining the level of risk transmission of filariasis in Pekalongan City which may help to adopt effective control strategies in filariasis eradication programs in Pekalongan City.

An Accurate Geocoding Method for GB-SAR Images Based on Solution Space Search and Its Application in Landslide Monitoring

Although ground-based synthetic aperture radar (GB-SAR) interferometry has a very high precision with respect to deformation monitoring, it is difficult to match the fan-shaped grid coordinates with the local topography in the geographical space because of the slant range projection imaging mode of the radar. To accurately identify the deformation target and its position, high-accuracy geocoding of the GB-SAR images must be performed to transform them from the two-dimensional plane coordinate system to the three-dimensional (3D) local coordinate system. To overcome difficulties of traditional methods with respect to the selection of control points in GB-SAR images in a complex scattering environment, a high-resolution digital surface model obtained by unmanned aerial vehicle (UAV) aerial photogrammetry was used to establish a high-accuracy GB-SAR coordinate transformation model. An accurate GB-SAR image geocoding method based on solution space search was proposed. Based on this method, three modules are used for geocoding: framework for the unification of coordinate elements, transformation model, and solution space search of the minimum Euclidean distance. By applying this method to the Laoguanjingtai landslide monitoring experiment on Hailuogou Glacier, a subpixel geocoding accuracy was realized. The effectiveness and accuracy of the proposed method were verified by contrastive analysis and error assessment. The method proposed in this study can be applied for accurate 3D interpretation and analysis of the spatiotemporal characteristic in GB-SAR deformation monitoring and should be popularized.

Weighted Geocoding Method Based on Chinese Word Segmentation and Its Application to Spatial Positioning of COVID-19 Epidemic Prevention and Control

Locating the coronavirus disease 2019 (COVID-19)cases in the accurate place is important in epidemic prevention and control. Geocoding is an effective method to achieve information space positioning with address description. The English based geocoding methodology is not suitable for Chinese address. Composition and positioning methods of Chinese address are discussed. According to the characteristics of high complexity and diversity of Chinese address, a Chinese word segmentation weighted address matching algorithm considering a variety of semantics is designed, including the same pronunciation but different Chinese word address, abbreviation and alias of Chinese address, different description of the same address. And the matching accuracy and efficiency of the algorithm are tested by using the COVID-19 cases' addresses in Wuhan. The result indicates the algorithm is efficient, accurate, and intelligent, which can realize the efficient location of the COVID-19 cases address, and provide accurate spatial location information for epidemic prevention and control by quickly positioning of the COVID-19 cases.

Side Scan Sonar Image Geocoding based on Carrier Velocity Coarseness Correction

Gross error may occur in Geocoded side scan sonar image due to complicated water bottom environments and AUV maneuver. This paper presents a side-scan sonar image geocoding method based on carrier speed gross error correction. This method uses the side scan sonar image imaging principle and SIFT feature matching algorithm to calculate the AUV lateral velocity. This lateral velocity is used to detect and eliminate the gross speed error of DVL in underwater integrated navigation, and to perform gross error correction on the side-scan sonar image geocoding. This method can improve the performance of AUV underwater integrated navigation, and significantly improve the accuracy of side-scan sonar image geocoding.

Do Patient Sociodemographic Factors Impact the PROMIS Scores Meeting the Patient-Acceptable Symptom State at the Initial Point of Care in Orthopaedic Foot and Ankle Patients?

AbstractBackgroundPatient-reported outcome measures such as the Patient-Reported Outcomes Measurement Information System (PROMIS) allow surgeons to evaluate the most important outcomes to patients, including function, pain, and mental well-being. However, PROMIS does not provide surgeons with insigh

Bringing historical British Population Census records into the 21st century: A method for geocoding households and individuals at their early‐20th‐century addresses

AbstractIncreased use of geographical information systems (GISs) for social scientific research has highlighted the analytical possibilities offered by unlocking historical data sources that are impractical with contemporary data. Genealogists and social scientific researchers have used information relating to individuals, households, and their addresses contained in the British historical census records for a number of years. This article takes geographical exploration of these a stage further by developing a method of attaching grid references to the census addresses, thereby opening up new research possibilities including spatial analysis. The resultant geocoded census data enable address‐, household‐, and individual‐level historical research and aggregation to contemporary and historical spatial units for exploration of demographic and socio‐economic change. The paper focuses on the development of a method of geocoding 1901 and 1911 Census data in respect of six study areas in the historical counties of London and Middlesex with over 260,000 individuals within some 60,000 households or communal establishments in both 1901 and 1911. Successful semi‐automated matching of historical census addresses with a contemporary address database is related to population density, change from property naming to numbering, residential development, thoroughfare name changes, and transcription error. Complete geocoding was completed by manual digitising with the aid of ancillary information sources and geographical information embedded in census records. The method outlined has the potential to be replicated in other areas and be adapted to use with other contemporary address databases that contain grid references. The paper outlines the factors that would influence transferability of the geocoding method.

Using First Name Information to Improve Race and Ethnicity Classification

ABSTRACTThis article uses a recent first name list to develop an improvement to an existing Bayesian classifier, namely the Bayesian Improved Surname Geocoding (BISG) method, which combines surname and geography information to impute missing race/ethnicity. The new Bayesian Improved First Name Surname Geocoding (BIFSG) method is validated using a large sample of mortgage applicants who self-report their race/ethnicity. BIFSG outperforms BISG, in terms of accuracy and coverage, for all major racial/ethnic categories. Although the overall magnitude of improvement is somewhat small, the largest improvements occur for non-Hispanic Blacks, a group for which the BISG performance is weakest. When estimating the race/ethnicity effects on mortgage pricing and underwriting decisions with regression models, estimation biases from both BIFSG and BISG are very small, with BIFSG generally having smaller biases, and the maximum a posteriori classifier resulting in smaller biases than through use of estimated probabilities. Robustness checks using voter registration data confirm BIFSG's improved performance vis-a-vis BISG and illustrate BIFSG's applicability to areas other than mortgage lending. Finally, I demonstrate an application of the BIFSG to the imputation of missing race/ethnicity in the Home Mortgage Disclosure Act data, and in the process, offer novel evidence that the incidence of missing race/ethnicity information is correlated with race/ethnicity.

Systematic data ingratiation of clinical trial recruitment locations for geographic-based query and visualization

BackgroundPrior studies of clinical trial planning indicate that it is crucial to search and screen recruitment sites before starting to enroll participants. However, currently there is no systematic method developed to support clinical investigators to search candidate recruitment sites according to their interested clinical trial factors. ObjectiveIn this study, we aim at developing a new approach to integrating the location data of over one million heterogeneous recruitment sites that are stored in clinical trial documents. The integrated recruitment location data can be searched and visualized using a map-based information retrieval method. The method enables systematic search and analysis of recruitment sites across a large amount of clinical trials. MethodsThe location data of more than 1.4 million recruitment sites of over 183,000 clinical trials was normalized and integrated using a geocoding method. The integrated data can be used to support geographic information retrieval of recruitment sites. Additionally, the information of over 6000 clinical trial target disease conditions and close to 4000 interventions was also integrated into the system and linked to the recruitment locations. Such data integration enabled the construction of a novel map-based query system. The system will allow clinical investigators to search and visualize candidate recruitment sites for clinical trials based on target conditions and interventions. ResultsThe evaluation results showed that the coverage of the geographic location mapping for the 1.4 million recruitment sites was 99.8%. The evaluation of 200 randomly retrieved recruitment sites showed that the correctness of geographic information mapping was 96.5%. The recruitment intensities of the top 30 countries were also retrieved and analyzed. The data analysis results indicated that the recruitment intensity varied significantly across different countries and geographic areas. ConclusionThis study contributed a new data processing framework to extract and integrate the location data of heterogeneous recruitment sites from clinical trial documents. The developed system can support effective retrieval and analysis of potential recruitment sites using target clinical trial factors.

The impact of geocoding method on the positional accuracy of residential burglaries reported to police

PurposeThe purpose of this paper is to better understand the variability in burglary geocoding positional accuracy between United States Census Topologically Integrated Geographic Encoding and Referencing (TIGER)-based street geocoding and results produced using reference data made publicly available by Google.Design/methodology/approachThis research compares the Euclidian distance between ground-truthed burglaries and results produced using two different geocoding reference data sets: TIGER-based street geocoding and publicly available data within Google Earth. T-tests and z-tests are used to discern whether positional errors are statistically significant.FindingsBoth within suburban and urban jurisdictions, Google outperformed street geocoding in terms of positional accuracy. Positional errors on average were 1/4th as large for Google in a suburban setting and 1/5th as large in an urban setting compared to street geocoding.Practical implicationsPolice departments that are relying on street geocoding techniques may achieve improved spatial precision by using Google’s reference data if they contain parcel-level information. Moreover, relying on less precise spatial referencing methods may place burglaries in locations where the events do not actually occur or cluster.Originality/valueThis is the first analysis of law enforcement data to examine the positional accuracy of geocoded offense data using Google Earth compared to the commonly used street geocoding method of interpolation.

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.

Using Routinely Collected Administrative Data in Public Health Research: Geocoding Alcohol Outlet Data

We describe our process of geocoding alcohol outlets to create a national longitudinal exposure dataset for Wales, United Kingdom from 2006 to 2011. We investigated variation in the availability of data items and the quality of alcohol outlet addresses held within unitary authorities. We used a standard geocoding method augmented with a manual matching procedure to achieve a fully spatially referenced dataset. We found higher quality addresses are held for outlets based in urban areas, resulting in the automatic geocoding of 68 % of urban outlets, compared to 48 % in rural areas. Missing postcodes and a lack of address structure contributed to a lower geocoding proportion. An urban rural bias was removed with the development of a manual matching procedure. Only one-half of the unitary authorities provided data on on/off sales and opening times, which are important availability factors. The resulting outlet dataset is suitable for contributing to the evidence-base of alcohol availability and alcohol-related harm. Local government should be encouraged to use standardised data fields, including addresses, to enable accurate geocoding of alcohol outlets and facilitate research that aims to prevent alcohol-related harm. Standardising data collection would enable efficient secondary data reuse using record linkage techniques, allowing the retrospective creation and evaluation of population-based natural experiments to provide evidence for policy and practice.

우리나라 도로명주소를 활용한 지오코딩 및 역 지오코딩 기법 개발

In Korea, the address-point matching technique has been used to provide geocoding services. In fact, this technique brings the high positional accuracy. However, the quality of geocoding result can be limited, since it is significantly affected by data quality. Also, it cannot be used for the 3D address geocoding and the reverse geocoding. In order to alleviate issues, the paper has implemeted proposed geocoding methods, based on streetbased addresses matching technique developed by US census bureau, for street-based addresses in Korea. Those proposed geocoding methods are illustrated in two ways; (1) street address-matching method, which of being used for not only 2D addresses representing a single building but also 3D addresses representing indoor space or underground building, and (2) reverse geocoding method, whichas converting a location point to a readable address. The result of street-based address geocoding shows 82.63% match rates, while the result of reverse geocoding shows 98.5% match rates within approximately 1.7(m) the average position error. According to the results, we could conclude that the proposed geocoding techniques enable to provide the LBS(Location Based Service). To develop the geocoding methods, the study has perfoermed by ignoring the parsing algorithms for address standardization as well as the several areas with unusual addresses, such as sub-urban areas or subordinate areas to the roads, etc. In the future, we are planning the improved geocoding methods for considering these cases.

Analysis of population damage by space debris upon collision with Earth based on the reverse geocoding method

Approximately 900 satellites currently orbit Earth. When satellites complete their mission, they are either intentionally deorbited or placed in orbits that will cause them to crash into Earth within 25 years after mission completion based on the rules set by the Inter-Agency Space Debris Coordination Committee. When a satellite reenters Earth's atmosphere, small or low-density components from its structure will mostly be incinerated. However, if large components remain, they may cause damage when they crash into the ground. These risks can cause population damage. In this study, reverse geocoding was used to perform a probability analysis on the population casualties for four cases: a single piece of debris, a single piece of debris with the Monte Carlo method, a satellite, and a satellite with the Monte Carlo method. The reverse geocoding method was found to be more accurate at analyzing the casualty probability than Debris Risk Assessment and Mitigation Analysis (DRAMA) because it uses population data based on the longitude as well as the latitude.

Supplementing Missing Self-Reported Race Data with a Probability Distribution in Logistic Regression Models

Race is often included as an independent variable in health services research, especially in recent studies of racial and ethnic disparities in health care. Although self-reported information on race exists in large electronic health records (EHR) data, these data are sometimes missing. Recently Bayesian Improved Surname Geocoding method (BISG) is used to estimate the probability distribution of race categories for those with missing information on race. The BISG estimated probability distribution has been used in reporting health care measures but not in statistical modellings with dichotomous events as outcomes. We propose two approaches to accommodate available distribution probability of an independent categorical variable (e.g., race) in logistic regression models: 1) a direct substitution approach and 2) a partial information maximum likelihood estimator (PIMLE). In examining the association between race and up-to-date immunization status of children by three years old from an integrated health care organization, 11.3% of 14,903 children have missing self-reported race information but have BISG estimated probability distribution for the six race/ethnicity categories. We employed the direct substitution approach and PIMLE approach to analyze the under vaccination data. Both approaches included all observations and thus yielded smaller standard errors of estimated coefficients compared to the complete data analyses. Our simulation study showed that the direct substitution approach and PIMLE yielded nearly unbiased coefficient estimates and preserved efficiency when the missing rate of the independent categorical variable was up to 30%.

Using machine-coded event data for the micro-level study of political violence

Machine-coded datasets likely represent the future of event data analysis. We assess the use of one of these datasets—Global Database of Events, Language and Tone (GDELT)—for the micro-level study of political violence by comparing it to two hand-coded conflict event datasets. Our findings indicate that GDELT should be used with caution for geo-spatial analyses at the subnational level: its overall correlation with hand-coded data is mediocre, and at the local level major issues of geographic bias exist in how events are reported. Overall, our findings suggest that due to these issues, researchers studying local conflict processes may want to wait for a more reliable geocoding method before relying too heavily on this set of machine-coded data.