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Geocoding is the procedure of finding the mapping between the Synthetic Aperture Radar (SAR) image and the imaged scene. The inverse form of the Range-Doppler (RD) model has been adopted to approximate the geocoding results. However, with advances in SAR imaging geodesy, its imprecise nature becomes more perceptible. The forward RD model gives reliable solutions but is time-consuming and unable to detect geometric distortions. This study proposes a highly optimized forward geocoding method to find the precise ground position of each image sample with a Digital Elevation Model (DEM). By following the intersection of the terrain and the so-called solution surface of an azimuth line, which can be locally approximated by a plane, it produces geo-location results almost identical to the analytical solutions of the RD model. At the same time, the non-unique geocoding solutions and the geometric distortions are determined. Deviations from the employed approximations are assessed, showing that they are highly predictable and lead to negligible range/azimuth residuals. The general robustness is verified by experiments on SAR images of different resolutions covering diversified terrains in the native or zero Doppler geometry. Comparisons with other forward algorithms demonstrate that, with extra geometric distortions detection ability, its accuracy and efficiency are comparable to them. For a Sentinel-1 IW burst of high topographic relief, the algorithm ends in a 3 s using 16 parallel cores, with an average residual smaller than one millimeter. Its impressive blend of efficiency, accuracy, and geometric distortion detection capabilities makes it ideal for large-scale remote sensing applications. [ABSTRACT FROM AUTHOR]

The process of engineering construction typically impacts the nearby existing buildings, causing either localized or widespread deformation of these structures. The application of deformation monitoring technology enables the timely identification of damage or uneven deformation, thereby facilitating the implementation of appropriate maintenance and repair actions. This paper explores the application of ground-based synthetic aperture radar (GB-SAR) in building facade deformation monitoring. GB-SAR acquires two-dimensional (2D) images and measures deformation along the line of sight (LOS) direction through interference calculation. In order to accurately determine the location of deformation and retrieve the elevation information of image pixels, it is usually required to utilize external three-dimensional (3D) data to reconstruct the 3D coordinate of these pixels, which is known as geocoding, or georeferencing. In the case of the GB-SAR system, it is necessary for the observer to measure the angles between the antenna orientation, the radar movement direction and the reference plane of the local surveying coordinate system. Nevertheless, the measurement of these attitude angles may be overlooked in practice, and the impact of small attitude angles or their measurement errors on GB-SAR geocoding may be disregarded, which directly affects the slant range projection (SRP) calculation of the external 3D data, thereby leading to biased 3D coordinate reconstruction of GB-SAR pixels. This paper proposes a method to calculate the SRP coordinates of point cloud data considering the attitude inclination angles of radar sensor, and perform matching calculations on the high-quality pixels (HQPs) with the point cloud after SRP to extract building facade deformation. The results demonstrate that the method is capable of effectively mitigating the influence of the radar attitude inclination angles, resulting in a reduction of the average deviation of 3D coordinates to approximately one-third of the value without consideration of the sensor's attitude in a real-world building monitoring experiment. [ABSTRACT FROM AUTHOR]

Objective This communication presents the results of defining a tribal health jurisdiction by a combination of tribal affiliation (TA) and case address. Materials and Methods Through a county-tribal partnership, Geographic Information System (GIS) software and custom code were used to extract tribal data from county data by identifying reservation addresses in county extracts of COVID-19 case records from December 30, 2019, to December 31, 2022 (n  = 374 653) and COVID-19 vaccination records from December 1, 2020, to April 18, 2023 (n  = 2 355 058). Results The tool identified 1.91 times as many case records and 3.76 times as many vaccination records as filtering by TA alone. Discussion and Conclusion This method of identifying communities by patient address, in combination with TA and enrollment, can help tribal health jurisdictions attain equitable access to public health data, when done in partnership with a data sharing agreement. This methodology has potential applications for other populations underrepresented in public health and clinical research. [ABSTRACT FROM AUTHOR]

Background and Aims Design Setting Participants Measurements Findings Conclusions Patient initiated transport refusal during Emergency Medical Service (EMS) opioid overdose encounters has become an endemic problem. This study aimed to quantify circumstantial and environmental factors which predict refusal of further care.In this cross‐sectional analysis, a case definition for opioid overdose was applied retrospectively to EMS encounters. Selected cases had sociodemographic and situational/incident variables extracted using patient information and free text searches of case narratives. 50 unique binary variables were used to build a logistic model.Prehospital EMS overdose encounters in Washington, DC, USA, from July 2017 to July 2023.Of EMS encounters in the study timeframe, 14 587 cases were selected as opioid overdoses.Predicted probability for covariates was the outcome variable. Model performance was assessed using Stratified K‐Fold Cross‐Validation and scored with positive predictive value, sensitivity and F1. Prediction accuracy and McFadden's pseudo‐R squared are also determined.The model achieved a predictive accuracy of 78% with a high positive predictive value (0.83) and moderate sensitivity (0.68). Bystander type influenced the refusal outcome, with decreased refusal probability associated with family (nondescript) (−28%) and parents (−16%), while presence of a girlfriend increased it (+28%). Negative situational factors like noted physical trauma (−62%), poor weather (−14%) and lack of housing (−14%) decreased refusal probability. Characteristics of the emergency response team, like a prior crew member encounter (+20%) or crew experience <1 year (−36%), had a variable association with transport.Refusal of emergency transport for opioid overdose cases in Washington, DC, USA, has expanded by 43.8% since 2017. Several social, environmental and systematic factors can predict this refusal. Logistic regression models can be used to quantify broad categories of behavior in surveillance medical research. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

AbstractToponym resolution is crucial for extracting geographic information from natural language texts, such as social media posts and news articles. Despite the advancements in current methods, including state-of-the-art deep learning solutions like GENRE and a sophisticated voting system that integrates seven individual methods, further enhancing their accuracy is essential. To achieve this goal, we propose a novel method that combines lightweight and open-source large language models and geo-knowledge. Specifically, we first fine-tune Mistral (7B), Baichuan2 (7B), Llama2 (7B & 13B), and Falcon (7B) to estimate toponyms’ unambiguous reference (e.g., city, state, country) given their contexts. Subsequently, we correct inaccuracies in generated references and determine their geo-coordinates via sequentially querying GeoNames, Nominatim, and ArcGIS geocoders until a successful geocoding result is achieved. Our methods demonstrate enhanced performance compared to 20 existing methods, as evidenced across seven challenging datasets including 83,365 toponyms worldwide, with the Mistral-based method leading, followed by Baichuan2, Llama2, and Falcon-based methods. Specifically, the Mistral-based method achieves an Accuracy@161km of 0.91, surpassing GENRE, the best individual method, by 17% and the seven-methods composite voting system by 7%. Moreover, our methods are computationally efficient, operable on one general GPU, have modest memory requirements (14 GB for 7B models and 27 GB for 13B models), and exceed both GENRE and the voting system in inferring speed. [ABSTRACT FROM AUTHOR]

Pathogenic mutations in phosphatase and tensin homolog (PTEN), a tumor suppressor gene, leads to the development of PTEN Hamartoma Tumor Syndrome (PHTS). Patients carry significantly increased risks for cancer and neurodevelopmental disorders such as autism spectrum disorder (ASD). The factors which modify PTEN function and drive PHTS phenotype remain unknown. Social determinants of health are increasingly being identified as important regulators of disease pathogenesis. Here, we conduct a retrospective cross-sectional study to evaluate the association between social deprivation, as measured by area deprivation index (ADI), and neurodevelopmental versus cancer phenotypes in patients with PHTS. Our findings suggest ADI alone is not associated with PHTS phenotype, and that its divergence is likely multifactorial in nature. More broadly, our study highlights the importance of integrating environmental pressures with genetic risk modification in the study of complex human disease.

Neighborhood-level disadvantage during childhood is a determinant of health that is hypothesized to confer risk for psychopathology via alterations in neuro-affective processing, including reward responsiveness. However, little research has examined the impact of socioeconomic disadvantage assessed at the community-level on reward processing, which may have important implications for targeted dissemination efforts. Furthermore, not all youth exposed to neighborhood disadvantage may exhibit alterations in reward reactivity, highlighting the need to consider factors that may exacerbate risk for blunted reward reactivity. The current study examined associations between geocoded indices of neighborhood disadvantage and electrocortical reward responsivity in youth and tested whether findings were moderated by maternal history of depression. The sample included 137 youth recruited for studies on the intergenerational transmission of depression. Neighborhood disadvantage was assessed using the Area Deprivation Index (ADI) while the reward positivity (RewP), an event-related potential, indexed reward response. Results revealed a significant interaction between ADI and maternal history of depression on youth RewP, such that greater neighborhood disadvantage was significantly associated with lower reward responsiveness, but only for youth with a maternal history of depression. Results were maintained controlling for youth internalizing symptoms and individual-level socioeconomic factors. Findings suggest that neighborhood disadvantage may impact youth neural reward processing, at least partially independently of individual risk factors, for youth with a maternal history of depression. If replicated, results suggest intervention efforts may be implemented at the community level to enhance reward responsiveness, specifically for youth living in low-resourced neighborhoods with a maternal history of depression. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Currently, deliveries have increased rapidly due to the development of delivery applications and the COVID-19 pandemic. In particular, the proportion of delivery sales for daily food has surged, with one coffee shop experiencing a 660% increase in delivery sales over the course of a year. However, this rapid rise in deliveries has led to a significant problem: cleanliness. When ordering delivery, users cannot check the store's kitchen and must trust the delivery application to place their order. In this paper, we develop an application that helps delivery users choose clean and safe food. By linking public data on administrative dispositions from the Ministry of Food and Drug Safety, users will be able to check and select establishments based on their administrative records directly on a map. [ABSTRACT FROM AUTHOR]

Human immunodeficiency virus (HIV) burden among Florida adolescents and young adults (AYA, aged 13–24 years), particularly in Tampa Bay, is among the highest in the nation. We sought to determine the association between zip code-level test site accessibility and AYA HIV burden, compare this association with adult (aged 25–44 years) HIV burden, and identify local AYA HIV testing deserts. We further aimed to identify the association between test site accessibility and population-level markers of social disadvantage. We geocoded HIV test sites and determined the percent surface area per zip code within 15-minute walking distance to ≥ 1 test sites (PSA15) in Pinellas and Hillsborough counties. We calculated Pearson's correlation coefficients for the association of PSA15 and HIV burden by age group and, separately, the association of PSA15 and population-level characteristics. Of the 96 zip codes analyzed, 36.5% had a PSA15 for HIV testing of 0%. The association between PSA15 and HIV burden was substantially higher for adults (r = 0.51, p <.001) than for AYA (r = 0.09, p =.38). Overall, we identified four potential AYA testing deserts. We also found that greater PSA15 was correlated with greater %Black/African-American residents (r = 0.32, p =.002), greater %residents living in poverty (r = 0.27, p =.008), and lower child opportunity index scores (r = −0.29, p =.004). Walking-accessible HIV test sites in Tampa Bay were limited and geographically distributed largely based on adult HIV burden, population-level markers of social disadvantage, and among areas with higher percentages of Black/African-American residents. Test site distribution was less correlated with AYA HIV burden, leaving this population vulnerable across multiple testing deserts. [ABSTRACT FROM AUTHOR]

Geocoding converts unstructured geographic text into structured spatial data, which is crucial in fields such as urban planning, social media spatial analysis, and emergency response systems. Existing approaches predominantly model geocoding as a geographic grid classification task but struggle with the output space dimensionality explosion as the grid granularity increases. Furthermore, these methods generally overlook the inherent hierarchical structure of geographical texts and grids. In this paper, we propose a hierarchy-aware geocoding model based on cross-attention within the Seq2Seq framework, incorporating S2 geometry to model geocoding as a task for generating grid labels and predicting S2 tokens (labels of S2 grids) character-by-character. By incorporating a cross-attention mechanism into the decoder, the model dynamically perceives the address contexts at the hierarchical level that are most relevant to the current character prediction based on the input address text. Results show that the proposed model significantly outperforms previous approaches across multiple metrics, with a median and mean distance error of 41.46 m and 93.98 m, respectively. Furthermore, our method achieves superior results compared to others in regions with sparse data distribution, reducing the median and mean distance error by 16.27 m and 7.52 m, respectively, suggesting that our model has effectively mitigated the issue of insufficient learning in such regions. [ABSTRACT FROM AUTHOR]

The rapid spread of the COVID-19 pandemic necessitated unprecedented collective action against coronavirus disease. In this light,we are proposing a novel online platform for the visualization of epidemiological data incorporating social determinants for understanding the patterns associated with the spread of COVID-19. The current AI computational platform combines modeling methodologies along with temporal geospatial visualization of COVID-19 data, providing real-time sharing of graphic analytical simulation of vulnerable hotspots of recurrent (nowcasting) and emergent (forecasting) infections visualized on a spatiotemporal scale on geoportals. The proposed study will be a secondary data analysis of primary data accessed from the national portal (Indian Council of Medical Research (ICMR)) incorporating 766 districts in India. Epidemiological data related to spatiotemporal visualization of the demographic spread of COVID-19 will be displayed using a compartmental socio-epidemiological model, reproduction number R, epi-curve diagrams as well as choropleth maps for different levels of administrative and development units at the district levels. [ABSTRACT FROM AUTHOR]

In the digital transformation era, video media libraries' untapped potential is immense, restricted primarily by their non-machine-readable nature and basic search functionalities limited to standard metadata. This study presents a novel multimodal methodology that utilizes advances in artificial intelligence, including neural networks, computer vision, and natural language processing, to extract and geocode geospatial references from videos. Leveraging the geospatial information from videos enables semantic searches, enhances search relevance, and allows for targeted advertising, particularly on mobile platforms. The methodology involves a comprehensive process, including data acquisition from ARD Mediathek, image and text analysis using advanced machine learning models, and audio and subtitle processing with state-of-the-art linguistic models. Despite challenges like model interpretability and the complexity of geospatial data extraction, this study's findings indicate significant potential for advancing the precision of spatial data analysis within video content, promising to enrich media libraries with more navigable, contextually rich content. This advancement has implications for user engagement, targeted services, and broader urban planning and cultural heritage applications. [ABSTRACT FROM AUTHOR]

Geocoding systems are essential for the rapid processing of large volumes of spatial data. There are numerous geocoding systems, each of which divides the space into subspaces with a unique geometric shape and employs a different approach of creating its unique identifier - the geocode. The article's main goal is to demonstrate how existing equal angular geocoding system can be modified to become equal areas geocoding system. The research focuses on the main characteristics of equal angular geocoding systems. Although there are many geocoding systems available today, the study is limited to Geohash, one of the equal angular geocoding systems widely used in practice. We propose a novel approach called Hashgeo that has many practical implications. Many advantages (practical implications) of using cells with equal area have been identified, including: facilitating data comparison between cells of different latitudes, simplifying the process of visualizing data on an online map when creating so-called "heat maps", facilitating statistical analysis of data, preserving the "units per area" indicator when summarizing and aggregating data in a larger scale from data for smaller cells and others. The areas of the spherical segments and the cells within them are calculated using a suitable derived formula.

Abstract Background In response to citizens’ concerns about elevated cancer incidence in their locales, US CDC proposed publishing cancer incidence at sub-county scales. At these scales, confidence in patients’ residential geolocation becomes a key constraint of geospatial analysis. To support monitoring cancer incidence in sub-county areas, we presented summary metrics to numerically delimit confidence in residential geolocation. Results We defined a concept of Residential Address Discriminant Power (RADP) as theoretically perfect within all residential addresses and its practical application, i.e., using Emergency Dispatch (ED) Address Point Candidates of Equivalent Likelihood (CEL) to quantify Residential Geolocation Discriminant Power (RGDP) to approximate RADP. Leveraging different productivity of probabilistic, deterministic, and interactive geocoding record linkage, we simultaneously detected CEL for 5,807 cancer cases reported to North Carolina Central Cancer Registry (NC CCR)- in January 2022. Batch-match probabilistic and deterministic algorithms matched 86.0% cases to their unique ED address point candidates or a CEL, 4.4% to parcel site address, and 1.4% to street centerline. Interactively geocoded cases were 8.2%. To demonstrate differences in residential geolocation confidence between enumeration areas, we calculated sRGDP for cancer cases by county and assessed the existing uncertainty within the ED data, i.e., identified duplicate addresses (as CEL) for each ED address point in the 2014 version of the NC ED data and calculated ED_sRGDP by county. Both summary RGDP (sRGDP) (0.62–1.00) and ED_sRGDP (0.36–1.00) varied across counties and were lower in rural counties (p

The development of storage standards for databases of different natures and origins makes it possible to aggregate and interact with different data sources in order to obtain and show complex and thematic information to the end user. This article aims to analyze some possibilities opened up by new applications and hypothesize their possible developments. With this work, using the currently available Web technologies, we would like to verify the potential for the use of Linked Open Data in the world of WebGIS and illustrate an application that allows the user to interact with Linked Open Data through their representation on a map. Italy has an artistic and cultural heritage unique in the world and the Italian Ministry of Cultural Heritage and Activities and Tourism has created and made freely available a dataset in Linked Open Data format that represents it. With the aim of enhancing and making this heritage more usable, the National Research Council (CNR) has created an application that presents this heritage via WebGIS on a map. Following criteria definable by the user, such as the duration, the subject of interest and the style of the trip, tourist itineraries are created through the places that host this heritage. New possibilities open up where the tools made available by the Web can be used together, according to pre-established sequences, to create completely new applications. This can be compared to the use of words, all known in themselves, which, according to pre-established sequences, allow us to create ever new texts. [ABSTRACT FROM AUTHOR]

The present structure of polling units was created in 1996 by the defunct National Electoral Commission of Nigeria (NECON), which created 120,000 polling units and 8,809 wards (Registration Areas) to serve the purpose of registration center and voting exercise. Goal and Objectives: The aim of the study is to carryout mapping and spatial distribution of polling units to enhance voting progress in Akure North L.G.A of Akure, Ondo state, Nigeria. This is achieved through identifying and mapping of the existing polling units, performing spatial distribution and analysis, database creation and geocoding of the polling units. Methodology: Spatial coordinates of the existing polling units were acquired with the aid of Global Positioning System (GPS). These coordinates were plotted using ArcGIS 10.6 and geo-database was created. Queries were performed to examine the ownership of the polling unit's premises and also to determine polling units with over 500 registered voters. In addition, Geocoding was done to enhance a text-based description of the location of each polling unit. Nearest Neighbor Analysis (NNA) was applied to examine the kind of distribution of the polling units. The distribution pattern was determined using average nearest neighbor analysis. The result produce Z-score = - 8.843133 and P-value = 0.000000, the Nearest Neighbor Index (NNI) value is 0.551025 which describe the distribution of the polling units to be clustered. Results: The result shows that some polling units attributed to the study area are outside of the boundary, 63 of the polling unit's premises are privately owned while 43 are located in government facilities. It is hereby recommended that this study should be adopted to eradicate the barrier of identification and navigation to the polling units. [ABSTRACT FROM AUTHOR]

Recently, the economy has been hit with a recession that has resulted in increasing job losses and heightened job insecurity. This makes predicting job trends crucial for employers and employees alike. Social media is an innovative technology to mine for sentiment analysis to provide for nuanced and data-driven insights into the current employment status. The proposed work uses VADER, a lexicon and rulebased sentiment analysis tool for calculating sentiment scores for all tweets individually, and classifying them as positive, negative or neutral on the basis of these scores. The primary focus of the work is to 1) Calculate sentiments for tweets and analyse the impact of changes in trajectories in job trends and status of layoffs across various countries and 2) Perform geospatial analysis and machine learning algorithms to represent the current status of layoffs and compare accuracies of various models to highlight the most efficacious one respectively. Three algorithms were used in the study, namely Random Forest, Logistic Regression, and K-Nearest Neighbours, out of which Logistic Regression yielded the highest accuracy of 91.88%. [ABSTRACT FROM AUTHOR]

Objective: To investigate the impact of neighborhood‐level social vulnerability on pediatric tonsillectomy outcomes. Methods: This single‐center retrospective cohort study included tonsillectomies performed on children aged 1 to 18 between August 2019 and August 2020. Geographic information systems were used to geocode addresses, and spatial overlays were used to assign census‐tract level social vulnerability index (SVI) scores to each patient. For categorical variables, two‐sided Pearson chi‐square tests were used, whereas for continuous variables, paired t‐tests, means, and standard deviations were calculated. SVI and its four subthemes were investigated using binomial logistic regressions to determine their impact on post‐T&A complications and readmissions. Results: The study included 397 patients, with 52 having complications (13.1%) and 33 (8.3%) requiring readmissions due to their complications. Controlling for age, gender, race, insurance status, surgical indication, comorbidities, obesity, and obstructive sleep apnea, postoperative complications were associated with high overall SVI (odds ratio [OR] 5.086, 95% confidence interval [CI] 1.128–22.938), high socioeconomic vulnerability (SVI theme 1, OR 6.003, 95% CI 1.270–28.385), and high house composition vulnerability (SVI theme 2, OR 6.340, 95% CI 1.275–31.525). Readmissions were also associated with high overall SVI (10.149, 95% CI 1.293–79.647) and high housing/transportation vulnerability (SVI theme 4, OR 5.657, 95% CI 1.089–29.396). Conclusion: Social vulnerability at the neighborhood level is linked to poorer surgical outcomes in otherwise healthy children, suggesting a target for community‐based interventions. Because of the increased risk, it may have implications for preoperative decision‐making, treatment plans, and clinic follow‐ups. Level of Evidence: 3. Laryngoscope, 134:954–962, 2024 [ABSTRACT FROM AUTHOR]

Individuals with internalizing conditions such as depression or anxiety are at risk of sleep difficulties. Social–ecological models of sleep health propose factors at the individual (e.g., mental health) and neighborhood (e.g., crime risk) levels that contribute to sleep difficulties. However, these relationships have been under-researched in terms of internalizing conditions. Therefore, the current study comprised participants diagnosed with major depression (n = 24) or social anxiety (n = 35). Sleep measures included actigraphic variables (i.e., total sleep time, waking after sleep onset, sleep onset latency) and subjective sleep quality. Geocoding was used to assess nationally-normed crime risk exposure at the person level (e.g., murder, assault) and property level (e.g., robbery, burglary). Analyses consisted of independent t-tests to evaluate potential differences between diagnostic groups. To examine relationships, multiple regressions were used with internalizing symptoms, crime risk, and age as independent variables and sleep measures as the dependent variable. The t-test results revealed that groups differed in symptoms and age but not sleep or neighborhood crime. Regression results revealed crime risk positively corresponded with sleep onset latency but no other sleep measures. Also, only depression positively corresponded with total sleep time. Preliminary findings suggest exposure to crime and depression relate differentially to facets of sleep in individuals with internalizing conditions. [ABSTRACT FROM AUTHOR]

Scholars across disciplines frequently employ data on housing developments subsidized by the National Low Income Housing Tax Credit (LIHTC). We find that the geographic coordinates for these developments, generated by the U.S. Department of Housing and Urban Development (HUD), are frequently inaccurate. Using both the population of data from California and a national sample, we find that HUD-provided geocodes are inaccurate nearly half the time while Google-generated geocodes are almost always more accurate. However, while Google's geolocation is more likely to be accurate, when it is inaccurate, it deviates from the true location by a much greater distance than HUD. We therefore recommend that scholars use Google-generated geocodes for most research applications where the localized environment matters; however, in studies where observations are aggregated to a larger area, researchers may prefer to use HUD geocodes, which are more frequently inaccurate but typically by smaller distances. [ABSTRACT FROM AUTHOR]

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It is widely acknowledged that geographic data in Ghana and most of Sub-Sahara Africa is patchy, and lack geographic coverage and detail. This has implications for mobility and access to socioeconomic, and cultural opportunities which require address and location data typically provided through geocoding. A critical component of geocoding is a reference database to lookup matching address and location information. Accordingly, we investigate the suitability of, and the use of electoral polling stations to build a reference database for geocoding. This was undertaken by mapping their geographic coordinates, analyzing their geographic spread and detail, and outlining the database structure for building the reference database. Using the 2020 electoral polling station data for the Greater North area of Ghana, we found that 93% of the geographic coordinates can be obtained from open data sources. Also, there is high proximity with a nearest neighbor distance of 663 m, a reasonable service area of 4300 m, and a median distance of 3700 m from a community location. The widespread availability, large geographic spread and moderate detail, make polling stations a creditable data source for geocoding. It is recommended the polling station data be augmented with a detailed geographic data like those from landmarks and paratransit service. [ABSTRACT FROM AUTHOR]

OBJECTIVE: Through geocoding the physical residential address included in the electronic medical record to the census tract level, we present a novel model for concomitant examination of individual patient-related and residential context-related factors that are associated with patient-reported experience scores. SUMMARY BACKGROUND DATA: When assessing patient experience in the surgical setting, researchers need to examine the potential influence of neighborhood-level characteristics on patient experience-of-care ratings. METHODS: We geocoded the residential address included in the electronic medical record (EMR) from a tertiary care facility to the census tract level of Orange County, CA. We then linked each individual record to the matching census tract and use hierarchical regression analyses to test the impact of distinct neighborhood conditions on patient experience. This approach allows us to estimate how each neighborhood characteristic uniquely influences Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) scores. RESULTS: Individuals residing in communities characterized by high levels of socioeconomic disadvantage have the highest experience ratings. Accounting for individual patients characteristics such as age, gender, race/ethnicity, primary language spoken at home, length of stay, and average pain levels during their hospital stay, neighborhood-level characteristics such as proportions of people receiving public assistance influence the ratings of hospital experience (0.01, P

The spatial and semantic data of geographic addresses are extremely important for citizens, governments, and companies. The addresses can georeference environmental, economic, security, health, and demographic parameters in urban areas. Additionally, address components can be used by users to locate any point of interest (POI) with location-based systems (LBSs). For this reason, errors in address data can affect the geographic location of events, map representations, and spatial analyses. Thus, this paper presents the development of an algorithm for evaluating the quality of semantic and geographic information in any geospatial address dataset. The reference datasets are accessible using open data platforms or spatial data infrastructure (SDI) and volunteered geographic information (VGI), and both have been compared with commercial datasets using geocoding web services. Address quality analysis was developed using several open-source data science code libraries combined with spatial databases and geographic information systems. In addition, the quality of geographic addresses was evaluated by carrying out normalized tests in accordance with International Geospatial Standards (ISO 19157). Finally, this methodology assesses the quality of authorized and VGI address datasets that can be used for geocoding any relevant information in specific urban areas. [ABSTRACT FROM AUTHOR]

Rotator cuff tear arthropathy (CTA) carries a significant symptomatic burden for patients. Reverse shoulder arthroplasty (RSA) is an effective treatment intervention for CTA. Disparities in musculoskeletal medicine are well documented; however, there is a paucity of literature on how social determinants of health affect utilization rates. The purpose of this study is to determine how social determinants of health affect the utilization rates of RSA. A single-center retrospective review was conducted for adult patients diagnosed with CTA between 2015 and 2020. Patients were divided by those who underwent RSA and those who were offered RSA but did not undergo surgery. Each patient's zip code was used to determine the most specific median household income in the US Census Bureau database and compared to the multistate metropolitan statistical area median income. Income levels were defined by the US Department of Housing and Urban Development's (HUD's) 2022 Income Limits Documentation System and the Federal Reserve's (FED's) Community Reinvestment Act. Because of numeric restrictions, patients were grouped into racial cohorts of Black, White, and all other races. Patients of other races had significantly lower odds of continuing to surgery compared with White patients in models controlled for median household income (odds ratio [OR] 0.38, 95% confidence interval [CI] 0.18-0.81, P =.01), HUD's 3 income levels (OR 0.36, 95% CI 0.18-0.74, P =.01), and FED's income levels (OR 0.37, 95% CI 0.17-0.79, P =.01). There was no significantly different odds of going on to surgery between FED income levels and median household income levels, but when compared with those with low HUD income, those below median had significantly lower odds of going on to surgery (OR 0.43, 95% CI 0.23-0.80, P =.01). Although contradictory to reported health care utilization for Black patients, our study supports reported disparities in utilization for other ethnic minorities. These findings may suggest that improvements in utilization efforts targeted Black-identifying patients but not necessarily other ethnic minorities. The findings of this study can help providers understand how social determinants of health play a role in the utilization of care for CTA and direct mitigation efforts to reduce disparities in access to adequate orthopedic care. [ABSTRACT FROM AUTHOR]

This methodological note makes two novel contributions to spatial political and conflict research using grid data. First, it develops a methodological theory of how uncertainty specific to grid data affects inference. Second, it introduces a comprehensive robustness test on sensitivity to this uncertainty, implemented in R. The uncertainty stems from (1) establishing the correct size of grid cells, (2) deciding the correct locations where the dividing lines of grid data are drawn, and (3) a greater effect of measurement errors due to finer grid cells. The proposed test diversifies grid cell sizes, by aggregating original grid cells into a multiple of these grid cells. The test also varies the locations of the diving lines, by using different starting points of grid cell aggregation (e.g., starting the aggregation from the corner of the entire map or one grid cell of the original size away from the corner). I apply the test to Theisen et al. (Int. Secur. 36(3):79–106, 2011), who utilize the PRIO-GRID data (Tollefsen et al., J. Peace Res. 49(2):363–374, 2012), to substantiate its use. [ABSTRACT FROM AUTHOR]

Background: In response to citizens' concerns about elevated cancer incidence in their locales, US CDC proposed publishing cancer incidence at sub-county scales. At these scales, confidence in patients' residential geolocation becomes a key constraint of geospatial analysis. To support monitoring cancer incidence in sub-county areas, we presented summary metrics to numerically delimit confidence in residential geolocation. Results: We defined a concept of Residential Address Discriminant Power (RADP) as theoretically perfect within all residential addresses and its practical application, i.e., using Emergency Dispatch (ED) Address Point Candidates of Equivalent Likelihood (CEL) to quantify Residential Geolocation Discriminant Power (RGDP) to approximate RADP. Leveraging different productivity of probabilistic, deterministic, and interactive geocoding record linkage, we simultaneously detected CEL for 5,807 cancer cases reported to North Carolina Central Cancer Registry (NC CCR)- in January 2022. Batch-match probabilistic and deterministic algorithms matched 86.0% cases to their unique ED address point candidates or a CEL, 4.4% to parcel site address, and 1.4% to street centerline. Interactively geocoded cases were 8.2%. To demonstrate differences in residential geolocation confidence between enumeration areas, we calculated sRGDP for cancer cases by county and assessed the existing uncertainty within the ED data, i.e., identified duplicate addresses (as CEL) for each ED address point in the 2014 version of the NC ED data and calculated ED_sRGDP by county. Both summary RGDP (sRGDP) (0.62–1.00) and ED_sRGDP (0.36–1.00) varied across counties and were lower in rural counties (p < 0.05); sRGDP correlated with ED_sRGDP (r = 0.42, p < 0.001). The discussion covered multiple conceptual and economic issues attendant to quantifying confidence in residential geolocation and presented a set of organizing principles for future work. Conclusions: Our methodology produces simple metrics – sRGDP – to capture confidence in residential geolocation via leveraging ED address points as CEL. Two facts demonstrate the usefulness of sRGDP as area-based summary metrics: sRGDP variability between counties and the overall lower quality of residential geolocation in rural vs. urban counties. Low sRGDP for the cancer cases within the area of interest helps manage expectations for the uncertainty in cancer incidence data. By supplementing cancer incidence data with sRGDP and ED_sRGDP, CCRs can demonstrate transparency in geocoding success, which may help win citizen trust. Highlights: Confidence in patients' residential geolocation becomes a key constraint of geospatial analysis for small areas. The presented novel methodology uses a concept of Residential Address Discriminant Power (RADP) as theoretically perfect within all residential addresses and its practical application, i.e., Emergency Dispatch (ED) Address Point Candidates of Equivalent Likelihood (CEL) to quantify Residential Geolocation Discriminant Power (RGDP) to approximate RADP. RGDP can be summarized for cases across a specific area (sRGDP) and serve as a threshold of confidence in residential geolocation. The analysis of data from the NC Cancer Registry showed wide variability of sRGDP between NC counties. The proposed methods for sRGDP apply to countries besides the US, specifically, those with a system of ED address points. [ABSTRACT FROM AUTHOR]

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Converting a postal address to a coordinate, geocoding, is a helpful tool in many applications. Developing a geocoder tool is a difficult task if this tool relates to a developing country that does not follow a standard addressing format. The lack of complete reference data and non-persistency of names are the main challenges besides the common natural language process challenges. In this paper, we propose a geocoder for Persian addresses. To the best of our knowledge, our system, TehranGeocode, is the first geocoder for this language. Considering the non-standard structure of Persian addresses, we need to split the address into small segments, find each segment in the reference dataset, and connect them to find the target of the address. We develop our system based on address parsing and dynamic programming for this aim. We specify the contribution of our work compared to similar studies. We discuss the main components of the program, its data, and its results and show that the proposed framework achieves promising results in the field by finding 83\% of addresses with less than 300 meters error.

Geocoding geometrically rectifies a remote sensing image according to a specific map projection, and is an essential process for utilizing synthetic aperture radar (SAR) satellite images. The accuracy of geocoding is affected by various intercorrelated error sources. In this study, we propose a framework for improving the geocoding accuracy of SAR images. Our framework consists of two major theoretical and computational steps: 1) calculating and setting the error budget of the SAR image geocoding accuracy and checking their quality and 2) applying a geometric correction model if the quality is lower than the predefined threshold. Error budget analysis was performed by utilizing the law of variance propagation, considering the correlations among the primary error sources. During the second (geometric correction) step, the non-multicollinearity (N-MC) model, a ground control point (GCP)-based geometric correction model without multicollinearity, was proposed. Experiments were conducted using two KOMPSAT-5 SAR images from Daejeon City, Korea to verify the framework. The geocoding accuracy of SAR images #1 and #2 exceeded the error budgets of all confidence levels, except for the row direction of SAR image #2, despite the vendor’s internal calibration. In the second step, two SAR images were geometrically corrected by applying the N-MC model. The use of geometric correction improved the geocoding accuracy of the two SAR images by approximately two to five pixels in the row and column directions. The final geocoding accuracy of the SAR images was within the error budget.

This study analyzes the results of six natural-language-processing geoparsing tools, using a set of 120 articles, to determine which would be most useful to incorporate into Journalmap.org, a scientific literature discovery tool. The evaluation of accurate locations was performed against a set of manually-verified geographic locations in the selected set of articles. Geoparser accuracy was determined by comparing the planar distance between the geoparser result and verified geographic locations. Parser-determined locations less than or equal to 161 km (100 mi) from the known geographic location were considered accurate. The tools tested in this study produced varying degrees of accuracy ranging from 20% to 65%. Libraries may use these tools for geographic indexing of literature or for similar discovery use cases, but emerging large language models (LLMs) appear ready to offer potentially improved results in the future. [ABSTRACT FROM AUTHOR]

Twitter is a highly active microblogging platform globally, and in Thailand, it has secured the 10th rank for the highest user base in 2021. The platform is known for its rapid news dissemination capabilities and richness in topological and geolocation information. As such, the extraction of geospatial data from unstructured text has become an emerging field that has caught the attention of geospatial researchers. Geospatial extraction aims to leverage the location-based information present in textual data, which can be achieved through two methods, including toponym extraction and geocoding. The first involves identifying the geographic name from the text, and the second involves assigning the corresponding coordinates to the identified location. This study presents two key contributions. Firstly, a transformer-based tool was developed for extracting geo-names utilizing the BERT architecture, achieving an F1 score of 0.919 for overall accuracy. Secondly, the geocoding task was explored. The primary focus was to estimate the location of extracted geographic names that could not be matched with established online databases such as Google Geocoding, implying that the geographic names may not have existed or might not have been accurately identified. To achieve this objective, a geographic name dataset sourced from Twitter was compiled and utilized as a test dataset. The proposed approach involved the application of a clustering machine learning model, along with the utilization of topological properties, to develop a model for estimating the location of geographic names. The efficiency of the proposed method was assessed using Root Mean Square Error (RMSE) to compare the geographic coordinates of the estimated location from the model with the actual geographic coordinates of the location. Results revealed that the topology words model exhibited extremely high efficiency, with an RMSE of 0.947 km. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: attention-deficit/hyperactivity disorder (ADHD) is associated with both polygenic liability and environmental exposures, both intrinsic to the family, such as family conflict, and extrinsic, such as air pollution. However, much less is known about the interplay between environmental and genetic risks relevant to ADHD--a better understanding of which could inform both mechanistic models and clinical prediction algorithms. Methods: Two independent data sets, the population-based Adolescent Brain Cognitive Development Study (ABCD) (N = 11,876) and the case-control Oregon-ADHD-1000 (N = 1449), were used to examine additive (G + E) and interactive (GxE) effects of selected polygenic risk scores (PRS) and environmental factors in a cross-sectional design. Genetic risk was measured using PRS for nine mental health disorders/traits. Exposures included family income, family conflict/negative sentiment, and geocoded measures of area deprivation, lead exposure risk, and air pollution exposure (nitrogen dioxide and fine particulate matter). Results: ADHD PRS and family conflict jointly predicted concurrent ADHD symptoms in both cohorts. Additive-effects models, including both genetic and environmental factors, explained significantly more variation in symptoms than any individual factor alone (joint R² = .091 for total symptoms in ABCD; joint R² = .173 in Oregon-ADHD-1000; all delta-R² p-values <2e-7). Significant effect size heterogeneity across ancestry groups was observed for genetic and environmental factors (e.g., Q = 9.01, p = .011 for major depressive disorder PRS; Q = 13.34, p = .001 for area deprivation). GxE interactions observed in the full ABCD cohort suggested stronger environmental effects when genetic risk is low, though they did not replicate. Conclusions: Reproducible additive effects of PRS and family environment on ADHD symptoms were found, but GxE interaction effects were not replicated and appeared confounded by ancestry. Results highlight the potential value of combining exposures and PRS in clinical prediction algorithms. The observed differences in risks across ancestry groups warrant further study to avoid health care disparities. [ABSTRACT FROM AUTHOR]

Geocoding is a spatial analysis method that uses address information (e.g., street address, intersection, census tract, zip code, etc.) to determine geographical coordinates (latitude and longitude). In recent decades, geocoding has gone beyond its primary use for census and demographic information to novel applications in disaster risk reduction, even to earthquake early warning. Here I demonstrate the usefulness of geocoding techniques to earthquake early warning systems as applied to case studies that relied on survey response data and crowd-sourced video footage. These datasets were initially collected to understand the efficacy of tests conducted on ShakeAlert®, the earthquake early warning system for the West Coast of the United States, and how people behave during earthquakes, respectively. Geocoding these data can improve our overall technical understanding of the system, demonstrate whether individuals take protective actions such as ‘Drop, Cover, and Hold On’, and spotlight community demographics that the system is reaching or unintentionally missing. The combination of these social science datasets with geocoding information deepens our knowledge of these fundamentally human-centered systems, including how to improve the distribution of alerts for people and individuals with access and functional needs. In the future, this work may help verify U.S. Geological Survey ‘Did You Feel It?’ responses and seismic intensity, especially in regions with sparse seismic networks.

In the digital transformation era, video media libraries’ untapped potential is immense, restricted primarily by their non-machine-readable nature and basic search functionalities limited to standard metadata. This study presents a novel multimodal methodology that utilizes advances in artificial intelligence, including neural networks, computer vision, and natural language processing, to extract and geocode geospatial references from videos. Leveraging the geospatial information from videos enables semantic searches, enhances search relevance, and allows for targeted advertising, particularly on mobile platforms. The methodology involves a comprehensive process, including data acquisition from ARD Mediathek, image and text analysis using advanced machine learning models, and audio and subtitle processing with state-of-the-art linguistic models. Despite challenges like model interpretability and the complexity of geospatial data extraction, this study’s findings indicate significant potential for advancing the precision of spatial data analysis within video content, promising to enrich media libraries with more navigable, contextually rich content. This advancement has implications for user engagement, targeted services, and broader urban planning and cultural heritage applications.

Relationships that traverse sociodemographic categories may improve community attitudes toward marginalized groups and potentially protect members of those groups from stigma and discrimination. The present study evaluated whether internalized HIV stigma and perceived HIV-related discrimination in health care settings differ based on individual- and neighborhood-level characteristics of women living with HIV (WLHIV). We also sought to extend previous conceptual and empirical work to explore whether perceived HIV-related discrimination mediated the association between neighborhood racial diversity and internalized HIV stigma. A total of 1256 WLHIV in the Women's Interagency HIV Study (WIHS) attending 10 sites in metropolitan areas across the United States completed measures of internalized HIV stigma and perceived HIV-related discrimination in health care settings. Participants also provided residential information that was geocoded into Federal Information Processing Standard (FIPS) codes and linked with census-tract level indicators. In cross-sectional analyses, greater neighborhood racial diversity was associated with less internalized HIV stigma and less perceived HIV-related discrimination regardless of individual race. Neighborhood median income was positively associated with internalized HIV stigma and perceived discrimination, while individual income was negatively associated with perceptions of stigma and discrimination. In an exploratory mediation analysis, neighborhood racial diversity had a significant indirect effect on internalized HIV stigma through perceived HIV-related discrimination. An indirect effect between neighborhood income and internalized stigma was not supported. These findings suggest that greater neighborhood racial diversity may lessen HIV stigma processes at the individual level and that HIV stigma-reduction interventions may be most needed in communities that lack racial diversity.

“Can you tell me where San Jose is located?” “Uh! Do you know that there are more than 1700 locations named San Jose in the world?” The official name of a location is often not the name with which we are familiar. Spatial named entity (SNE) disambiguation is the process of identifying and assigning precise coordinates to a place name that can be identified in a text. This task is not always straightforward, especially when the place name in question is ambiguous for various reasons. In this context, we are interested in the disambiguation of spatial named entities that can be identified in a textual document on a country level. The solution that we propose is based on a set of techniques that allow us to disambiguate the spatial entity considering the context in which it is mentioned from a certain number of characteristics that are specific to it. The solution uses as input a textual document and extricates the named entities identified therein while associating them with the correct coordinates. SNE disambiguation is designed to support the process of fast exploration of spatiotemporal data analysis, most often for event tracking. The proposed approach was tested on 1360 SNEs extracted from the GeoVirus dataset. The results show that SNEToolkit outperformed the baseline, the standard Geonames geocoder, with a recall value of 0.911 against a recall value of 0.871 for the baseline. A flexible Python package is provided for end users.